This paper presents the design and implementation of adaptive control with approximate input–output linearization for underactuated open-loop unstable non-linear mechanical systems. Control of a ball and beam (BB) mechanism is selected as a benchmark problem for testing the designed control. The method of input–output linearization is reviewed and an adaptive input–output linearizing control design procedure is given. An approximate BB model is developed using Euler–Lagrange equations, and input–output linearization-based adaptive tracking control is designed for the system. The model is parameterized with respect to ball mass for adaptive tracking, and the proposed control structure is tested via computer simulations and experiments. The results present the tracking performance of designed control for various ball masses, and reveal the proposed method’s capability to cover ball mass variations over non-adaptive control. The proposed control exhibits improved error performance in the presence of parametric variations in the plant. Results of the BB control case reveal successful control of underactuated non-linear mechanisms when a system parameter is unknown or time varying.
In this paper, we investigate a tracking control problem for second-order multi-agent systems. Here, the leader is self-active and cannot be completely measured by all the followers. The interaction network associated with the leader–follower multi-agent system is described by a jointly connected topology, where the topology switches over time and is not strongly connected during each time subinterval. We consider a consensus control of the multi-agent system with or without time delay and propose two categories of neighbour-based control rules for every agent to track the leader, then provide sufficient conditions to ensure that all agents follow the leader, and meanwhile, the tracking errors can be estimated. Finally, some simulation results are presented to demonstrate our theoretical results.
In this article, we suggest an extension of our proposed method in fault detection called Reduced Kernel Principal Component Analysis (RKPCA) (Taouali et al., 2015) to fault isolation. To this end, a set of structured residues is generated by using a partial RKPCA model. Furthermore, each partial RKPCA model was performed on a subset of variables to generate structured residues according to a properly designed incidence matrix. The relevance of the proposed algorithm is revealed on Continuous Stirred Tank Reactor.
This paper addresses the performance limitation problem of networked systems by co-designing the controller and communication filter. The tracking performance index is measured by the energy of the error signal. Explicit expressions of the performance limitation are obtained by applying the controller and communication filter co-design, and the optimal network filter is obtained by applying the frequency domain method. It is shown that the performance limitation is closely related to the unstable poles and the non-minimum phase zeros of a given plant under the one-parameter compensator structure, whereas, under the two-parameter compensator structure, the performance limitation is unrelated to the unstable poles of a given plant. It is also demonstrated that the performance limitation can be improved and the effect of the channel noise can be eliminated by using the controller and communication filter co-design. Finally, some typical examples are presented to illustrate the theoretical results.
This paper proposes a new robust adaptive law for adaptive control of vehicle active suspensions with unknown dynamics (e.g. non-linear springs and piece-wise dampers), where precise estimation of essential vehicle parameters (e.g. mass of vehicle body, mass moment of inertia for the pitch motions) may be achieved. This adaptive law is designed by introducing a novel leakage term with the parameter estimation error, such that exponential convergence of both the tracking error and parameter estimation error may be proved simultaneously. Appropriate comparisons with several traditional adaptive laws (e.g. gradient and -modification method) concerning the convergence and robustness are presented. The mitigation of the vertical and pitch displacements can be achieved with the proposed control to improve the ride comfort. The suspension space limitation and the tyre road holding are also studied. A dynamic simulator consisting of commercial vehicle simulation software Carsim® and Matlab® is built to validate the efficacy of the proposed control scheme and to illustrate the improved estimation performance with the new adaptive law.
This paper considers the problem of delay-independent stabilization of linear fractional order (FO) systems with state delay. As in most practical systems in which the value of delay is not exactly known (or is time varying), a new approach is proposed in this paper, which results in asymptotic delay-independent stability of the closed-loop time-delay FO system. For this purpose, a novel FO sliding mode control law is proposed in which its main advantage is its independence to delay. Furthermore, a novel appropriate delay-independent sliding manifold is suggested. Additionally, two theorems are given and proved, which guarantee the occurrence of the reaching phase in finite time and the asymptotic delay-independent stability conditions of the dynamic equations in the sliding phase. Finally, in order to verify the theoretical results, two examples are given and simulation results confirm the performance of the proposed controller.
This paper studies the stability conditions of a discrete-time switched linear system in the presence of affine parametric uncertainties and an unknown time delay. Based on a discrete Lyapunov functional, sufficient conditions are investigated to determine the upper bound of admissible time delay in the discrete-time switched system. Furthermore, the average dwell time method, which is an effective tool for stability analysis of switched systems, is used to derive the exponential stability conditions. These conditions characterize the switching signal, which does not depend on any uncertainties. Finally, numerical examples are provided to verify and compare the theoretical results.
To fulfil the high precision requirements of the manoeuvre imaging mode of an agile satellite and solve the shortcomings of existing methods, a segmented attitude planning method based on the Pseudospectral method is proposed. First, a state equation was built by integrating the attitude constraint, time window and other restrictions. Furthermore, to guarantee the accuracy and stability of objects, a segmented strategy of an attitude trajectory approaching the imaging target using a constant angular velocity was proposed. The segmented time, three-axis position and velocity were also determined. Second, taking the global performance into account, the first section of the attitude trajectory was planned by the Legendre Pseudospectral method in order to acquire the optimal state variables and control variables. The second section of the attitude trajectory was then obtained by the constant angular velocity planning method. Next, in order to track the planning trajectory efficiently, a PD controller was designed that was suitable for a segmented planning method. High precision control parameters were obtained by the optimized method. Finally, numerical simulation results show that the proposed segmented attitude planning method successfully meets the high precision requirements of the manoeuvre imaging mode, and the feasibility and effectiveness of this method is verified.
For a high-speed rotor in a magnetically suspended control moment gyroscope (MSCMG), the gyroscopic effect is one of its prominent characteristics. To improve the control stability of this kind of high-speed rotor in an MSCMG, an extremely important parameter of the rotor named the inertia ratio is defined originally and its effects on the rotor’s gyroscopic effect are analysed theoretically by simplifying the rotor into a standard model during its mechanical design precessing. This research indicates that the inertia ratio of a rotor is determined not only by the rotor’s mass and material density, but also by its generalized stiffness and shape coefficient. For an MSCMG with speed 12,000 rev/min and angular momentum 200 Nms, the optimized range of the rotor’s inertia ratio can be selected as 1.780–1.9, which is more accurate than the experimental one (1.4–2.0). In this case, the precession frequency is reduced by 19.2%, the mass is reduced by 4.63% and angular momentum is increased by 24.45%. This paper will provide helpful hints for the design and optimization of a high-speed rotor.
In this paper, a perturbation observer-based adaptive passive control scheme is developed to provide great robustness of nonlinear systems against the unpredictable uncertainties and disturbances therein. The proposed scheme includes a high-gain perturbation observer and a robust passive controller. The high-gain perturbation observer is designed to estimate online the perturbation aggregated from the combinatorial effect of system nonlinearity, parameter uncertainty, unmodelled dynamics and fast time-varying external disturbances. Then the robust passive controller, using the estimated perturbation, can produce the minimal control effort needed to compensate for the magnitude of the actual current perturbation. Furthermore, the convergence of estimation error of the high-gain perturbation observer and the closed-loop system stability are analyzed theoretically. Finally, two practical examples are given to show the effectiveness and advantages of the proposed approach over the accurate model-based passive control scheme and the linearly parametric estimation-based adaptive passive control scheme.
The article deals with a novel approach to reactive navigation. A proposed reactive navigation is based on the Vector Field Histogram (VFH) method, which is easily modifiable. The biggest advantage of the proposed method is that it allows the robot to avoid static as well as moving obstacles in an unknown environment in a more effective way and without the need of switching any algorithm or the robot’s behaviour. Moreover, the proposed extension allows the avoidance of several moving obstacles in real time. The article contains three proposed improvements of the original VFH* method: representation of moving objects, look-ahead tree improvements and approach to criterion function. The method is verified by several experiments in various scenarios, which include few static and moving obstacles.
A redundant electro-hydraulic shaking table (REST) of six degrees-of-freedom (6 DOFs) with eight hydraulic actuators is an essential experimental tool in many industrial applications for real-time simulation of actual vibrations, such as structural vibration, earthquake simulation and fatigue testing. In order to obtain a high-fidelity acceleration waveform on the REST, a feed-forward inverse model (FFIM) controller with a modelling error compensator is proposed in this study. A recursive extended least-squares algorithm is employed to identify an acceleration closed-loop transfer function of the REST. A zero phase error compensation technology is employed to guarantee stability of the designed FFIM because the identified acceleration closed-loop transfer function is a typical non-minimum phase system and its direct inverse transfer function is unstable. The modelling error compensator is designed to compensate for the modelling error between the identified transfer function and the actual experimental REST, which deteriorates the acceleration waveform replication accuracy of the REST. A 6 DOF REST experimental system was used to verify the proposed controller. Experimental results demonstrated that the proposed controller gave satisfactory acceleration tracking performances on the REST.
A scheduling method based on fuzzy feedback priority and variable sampling period for a networked control system with resource constraints is proposed in this paper. Firstly, each control loop weight is adjusted dynamically according to a control loop data transmission error through a quadratic square root mapping function. A different weight is given for each loop. A fuzzy feedback priority scheduling strategy is designed combined with dynamic weight, system output and system output error rate. The priority of each control loop is adjusted dynamically. Then, considering the current network status, the new network utilization is predicted through proportional control. A least-squares support vector machine (LSSVM) algorithm is used for controlled loop packet transmission time prediction. A variable sampling period for each control loop is determined by using a controlled loop dynamic weight, network utilization and data transmission time. Finally, a fuzzy feedback priority scheduler and variable sampling period scheduler are combined. The merits of the two schedulers are utilized to achieve the optimization goal. A simulation experiment is carried out using the True Time toolbox. Simulation results show that the scheduling method can make the network utilization rate converge to the desired value under resource constraints, improve the output response performance of the control loop, reduce the integral absolute error value of the control loop and decrease the data transmission time of the control loop. The networked control system performance is improved and the scheduling method of this paper is effective.
This paper presents finite-time chaos synchronization of time-delay chaotic systems with uncertain parameters. According to the proposed method, a lot of coupled items can be treated as zero items. Thus, the whole system can be simplified greatly. Based on robust chaotic synchronization, secure communication can be realized with a wide range of parameter disturbance and time-delay. Numerical simulations are provided to illustrate the effectiveness of the proposed method.
The consensus in a multi-agent system (MAS) in which each agent communicates with the others over a wireless network is investigated. For the convenience of calculation, analysis and discussion, the MAS under fixed communication topology with communication constraints including random time delay, packet loss and environmental noise is transformed into an asynchronous dynamical system. Thus, the consensus control of the MAS is equivalent to the
In application systems of the Internet of Things (IoT), real-time monitoring and control of the systems need to be realized with wireless communications from machine to machine (M2M), especially under harsh application scenarios. The data link of the M2M communication has also a critical effect on the system in the application. In this paper, a wireless bi-directional data link for smart temperature recording is reported. The radio frequency hardware employed consists of NRF24L01 wireless transceivers, RFX2401 amplifiers and matching network sand antennas. The microcontroller unit (MCU) is based on the STM32F103VET6 chip, which controls the NRF24L01 by a four-pin serial peripheral interface (SPI) to realize the bi-directional data link, and the sensor data are sampled with 12-bit analogue-to-digital converters (ADCs). The communication is realized as the interrupt request (IRQ) signal of the NRF24L01 changes periodically. A look-up table and a linear optimization method are also implemented to improve the accuracy of ADC data. The bi-directional data link is then applied to a wired heating control system. The results show that temperature data can be transmitted and received over a distance up to 320 m in an open environment and 52 m in an indoor complex environment with the hardware implemented. The real-time temperature data can be displayed on a computer or a handheld device. Wireless M2M communication and control are thus demonstrated.
In this paper, a composite adaptive dynamic surface control scheme is developed for a class of parametric strict-feedback nonlinear systems. The proposed composite adaptation law uses both the surface error and the estimation error to update the parameters. In addition, by using the dynamic surface control technique, the problem of the explosion of complexity in the adaptive backstepping design is avoided. It is proved that the proposed scheme guarantees uniform ultimate boundedness of all signals in the closed-loop system with arbitrary small surface error by adjusting the design parameters. Simulation results demonstrate the effectiveness of the proposed approach for an electrohydraulic actuator system.
In this paper, a novel optimization approach to estimate the time delay and the parameters of Wiener time-delay systems is proposed. The proposed method consists first in defining a cost function and second in selecting an appropriate algorithm to solve it. However, any used cost function for the purpose of Wiener time-delay system identification presents several difficulties in terms of nonlinearity and inaccessible measurements. In fact, the hierarchical approach, the rounding property and the auxiliary model approach are suggested as solutions to overcome these difficulties. These solutions allow us to transform the cost function to be minimized into two simple cost functions that are minimized using the conjugate gradient algorithm with different choices of its main parameters. Simulation results are presented to illustrate the performance of the proposed approach.
The problem of projected work space trajectory synchronization for multiple two link flexible manipulators is considered here. Generalized projective synchronization between a controlled master and multiple slave manipulators is presented in this paper. The master and slave manipulators are non-identical. A low frequency chaotic signal and an exponentially varying signal are used as the desired trajectories. An equivalent sliding mode controller is designed for the master manipulator to track the desired trajectory. A modified adaptive equivalent sliding mode controller is designed for the slave manipulators to be projectively synchronized with the controlled master. Two scaling factors are used for the projective synchronization. Simulation results, with disturbances and payload variation reveal that the master and multiple slaves are synchronized with their respective desired trajectories. Such projective synchronization between one master and multiple slaves using the proposed control techniques to track a low frequency chaotic desired signal is not found in the literature. Such projective synchronization to track a chaotic signal is considered as the novelty of this paper. The performances of the proposed control techniques are found to be better in terms of link deflections and control effort when compared with three other sliding mode control techniques.
In this paper, a robustification method of the primary fractional controller is proposed. This novel method uses the adjustable fractional weights on the H mixed-sensitivity problem. It can achieve an enhancement in both nominal performance and robust stability margins for the uncertain plants while respecting the frequency-domain constraints, such as the tracking of the set-point references, load disturbance attenuation and measurement noise suppression. The proposed robustification holds two steps; in the first step, a primary fractional controller is designed from solving the H mixed-sensitivity problem that uses fixed-integer weights. In the second step, the robustified fractional controller with adjustable fractional weights is designed in order to guarantee a good compromise between the nominal performance and the robust stability not only for the nominal plant, but also for all set of the neighbouring plants. The proposed robustified fractional controller is used to control the doubly fed induction generator. Its dynamic is modelled by the unstructured output-multiplicative uncertain plant. Simulation results given by both primary and robustified fractional controllers are compared in time and frequency domains with those given by the conventional integer H controller in order to validate the effectiveness of the proposed method.
In this study, we aimed to obtain smoother wheel rotational acceleration during braking with an activated anti-lock brake system (ABS). This produces effective and easily controlled rotational acceleration of a wheel by an ABS control unit. For this, the wheel load is changed by considering the interaction between the brake pressure change rates and rotational acceleration of the wheel. This is provided by means of the control strategy developed in this study. The rules of the control strategy are based on ABS test results. These tests are conducted with soft, medium-hard and hard dampers on wet and slippery road surfaces. Therefore, the control strategy changes the wheel load by setting the damper stage according to agreement between brake pressure and wheel rotational acceleration. Here, the control strategy constantly applies the damping force of the damper providing the shortest braking distance under wet or slippery road conditions. All results show that the control strategy considerably improves wheel rotational acceleration oscillations during braking with an activated ABS.
Large cities have been facing serious problems in the management of traffic, owing to the increasing number of vehicles and pedestrians. Traffic engineering is essential in managing traffic and improving urban mobility. This paper deals with the problem of fixed-time signal programming on traffic networks. A new bi-objective optimization model is proposed to maximize the average and minimize the variance of the vehicle speeds in the network. Although the first function is commonly discussed in the literature, the second one is novel, and its aim is to provide flow balance along the network. This combination of functions is optimized by the Memory-Based Variable-Length Nondominated Sorting Genetic Algorithm 2 (MBVL-NSGA2), which avoids the revaluation of candidate solutions. This approach was validated through experiments using the microscopic simulator GISSIM, in a multi-intersection real network, using measured data from Belo Horizonte traffic engineering company (BHTRANS). The practical results of MBVL-NSGA2 were compared with four approaches: (1) current BHTRANS solutions; (2) a genetic algorithm optimizing the first function; (3) a genetic algorithm optimizing the second function, and; (4) the traditional NSGA2. Analysis showed that this proposal is able to generate better traffic signal plans, at the same time that it generates a diversified set of efficient candidate solutions.
This paper focuses on the problem of adaptive tracking control for switched nonlinear systems with time-delay and unknown functions under arbitrary switchings. Based on the adaptive backstepping technique and common Lyapunov function an approach to a class of adaptive fuzzy controllers is designed. The fuzzy logic system is used to approximate the unknown nonlinear functions. The proposed controller guarantees that the output can converge to a small neighbourhood of the reference signal and ensure all of the signals are bounded. A numerical example is provided to demonstrate the effectiveness of the paper.
In this paper, a proportional–integral–derivative (PID) controller design method for stable and integrating time-delay systems with and without non-minimum phase zero (inverse response) using the direct method is proposed. The PID controller gains are obtained by matching the frequency response of the closed-loop control system to that of the reference model with a minimum weighted integral squared absolute error in the bandwidth region. The reference model is chosen to satisfy the desired maximum sensitivity Ms. As a result, three linear algebraic equations in three unknowns are obtained and the solution of them gives the PID controller gains. The proposed method can be applied to low- and high-order systems, and the Pade approximation of the time-delay term e–Ls is not required.
The periodic discrete-time matrix equations have wide applications in stability theory, control theory and perturbation analysis. In this work, the biconjugate residual algorithm is generalized to construct a matrix iterative method to solve the periodic discrete-time generalized coupled Sylvester matrix equations
The constructed method is shown to be convergent in a finite number of iterations in the absence of round-off errors. By comparing with other similar methods in practical computation, we give numerical results to demonstrate the accuracy and the numerical superiority of the constructed method.
We are concerned with the fault-tolerant tracking control affair for a class of large-scale multi-input and multi-output (MIMO) nonlinear systems suffering from actuator failures. Taking advantage of the mean-value theory and the implicit function theorem, the non-affine subsystems are transformed into affine forms. Neural networks (NNs) are utilized to approximate unknown virtual control signals, and then an adaptive NN-based decentralized tracking control strategy is exploited recursively by combining backstepping methods as well as the dynamic surface control (DSC) methodology. In theory, the stability of the resulting whole system is rigorously analysed, where it is proven that all signals remain uniformly ultimately bounded (UUB) and the designed strategy can guarantee the convergence of tracking errors via a suitable choice of control parameters. Finally, two simulation examples, both practical and numerical examples, are illustrated to verify the feasibility of the theoretical claims.
A robustification method of primary two degree-of-freedom (2-DOF) controllers is proposed in this paper to control the wind turbine system equipped with a doubly-fed induction generator DFIG. The proposed robustification method should follow the following three step-procedures. First, the primary 2-DOF controller is designed through the initial form of the multivariable generalized predictive control MGPC law to ensure a good tracking dynamic of reference trajectories. Second, the robust
The solution of the nonhomogeneous Yakubovich matrix equation
To overcome the low operation efficiency, high labour-intensiveness and high risk in the artificial live-line replacement of insulator strings, a robot for overhead transmission line maintenance was developed. In order to suppress effectively the influences of disturbance signals and uncertainties on tracking precision and stability of the robot mechanical arm motion under high voltage and strong electromagnetic interference, this paper proposed a H control theory-based robust trajectory tracking control method for the robot mechanical arm. Through layering robot control architecture, a dynamic model of mechanical arm basic motion was established by the Lagrange method combined with an armature voltage equation of the joint motor, and the unified dynamic model of mechanical arm different motion was obtained. On this basis, the state-space model of mechanical arm motion error was deduced under disturbances and uncertainties, and thus an H control model for mechanical arm motion was constructed. Subsequently, the H controller for the mechanical arm trajectory tracking control system was solved by linear matrix inequality (LMI) based on the established model, and the asymptotic stability of the mechanical arm motion control system was verified by selecting the appropriate Lyapunov function. The proposed method for such a controller was proved to be of good versatility, strong adaptability and sound expansibility. Finally, simulation results verified the effectiveness of the H controller and field operation tests further validated the engineering practicability of such a control method in macro and micro aspects.
This paper investigates a finite-time attitude manoeuvre control problem for a flexible spacecraft subject to bounded external disturbances. A robust discontinuous finite-time controller with terminal sliding mode control is designed to solve this problem provided that the disturbances and the coupling effect of flexible modes are bounded with a known boundary. The controller is further enhanced by an adaptive scheme to deal with the more practical case that the boundary is unknown. The enhanced version is continuous and chattering-free. The results are rigorously proved using the Lyapunov stability theory. The effectiveness and robustness of the proposed controllers are demonstrated by numerical simulation.
The echo-state network is a new structure of recurrent neural networks. Based on the echo-state network, this paper develops an adaptive output feedback control method for a class of perturbed Sngle-Input Single-Output (SISO) nonlinear system in which only the system output is measured. The echo-state network is developed to approximate the control law based on the certainty equivalent approach. A Luenberger like observer is used to estimate the state signals. The echo-state network controller’s parameters are updated on-line using the gradient of descent method. The overall adaptive scheme guarantees that all signals involved are bounded and the output of the closed-loop system will asymptotically track the desired output trajectory without using a supervisory control term. Two nonlinear systems are used to verify the effectiveness of the proposed method.
The paper deals with the recursive identification of time-varying non-linear dynamic systems using three-block cascade models with non-linear static, linear dynamic and non-linear dynamic blocks. These models are appropriate for systems with both actuator and sensor non-linearities. Multiple application of a decomposition technique provides special expressions for the corresponding non-linear model description that are linear in parameters. A modified recursive least-squares-based algorithm is used for estimation of the time-varying input polynomial and output backlash parameters. Simulation studies show the feasibility of proposed approach to estimate the model parameters and track their changes.
This paper discusses the issue of the continuous state estimation for a class of uncertain nonlinear switched systems under the two cases of both average dwell time and mode-dependent average dwell time. A robust and adaptive switched observer is developed such that the states of an original nonlinear switched system can be asymptotically estimated, where the Lipschitz constant of the nonlinear term may be unknown since the designed adaptation law can adaptively adjust it. Based on the feasible solution of an optimization problem with a linear matrix inequality constraint, the observer gain matrices are obtained and guarantee the existence of a robust switched observer. Meanwhile, the switching signals are designed such that the observer error dynamics is globally uniformly exponentially stable, and the sufficient conditions of the existence of a robust sliding-mode switched observer are derived. Finally, the effectiveness of the proposed approaches is illustrated by a numerical example and switched Rössler chaotic dynamics.
In this paper, an observer-based sliding mode controller is proposed for a high-accuracy motion plant to suppress the disturbances and improve the tracking performance. In particular, a two time-scale separation technology, which can recover the disturbance state in a faster time scale, is utilized to compensate the disturbances and improve the system robustness. The parameter identification is carried out to obtain the model coefficients with a high fitting rate. Such an identified model can allow the engineers to tune the controller’s gains highly enough when the system suffers from the measurement noises. Instead of the traditional low-pass filter, a differentiator is introduced for the velocity signal prediction and its discrete-time version is provided to attenuate the noises effect. To verify the effectiveness of the proposed approach, an adaptive robust control law is compared with the proposed one in terms of dynamic positioning error, robustness and rapid signal tracking, and the superiority and advantages can be illustrated by the experimental results.
Flexible and stretchable electronics technologies have been attracting increasing attention owing to their potential applications in personal consumed electronics, wearable human–machine interfaces (HMI) and the Internet of Things (IoTs). This paper proposes an HMI based on a polyvinylidene difluoride (PVDF) sensor and laminated it onto the surface of the skin for signal classification and controlling the motion of a mobile robot. The PVDF sensor with ultra-thin stretchable substrate can make conformal contact with the surface of the skin for more accurate measurement of the electrophysiological signal and to provide more accurate control of the actuators. Microelectro-mechanical system (MEMS) technologies and transfer printing processes are adopted for fabrication of the epidermal PVDF sensor. Sensors placed on two wrists would generate two different signals with the fist clenched and loosened. It can be classified into four signals with a combination of the signals from both wrists, i.e. four control modes. Experiments demonstrated that PVDF sensors may be used as an HMI to control the motion of a mobile robot remotely.
This paper highlights a new method for the detection of ischaemic episodes using statistical features derived from ST segment deviations in electrocardiogram (ECG) signal. Firstly, ECG records are pre-processed for the removal of artifacts followed by the delineation process. Then region of interest (ROI) is defined for ST segment and isoelectric reference to compute the ST segment deviation. The mean thresholds for ST segment deviations are used to differentiate the ischaemic beats from normal beats in two stages. The window characterization algorithm is developed for filtration of spurious beats in ischaemic episodes. The ischaemic episode detection is made through the coefficient of variation (COV), kurtosis and form factor. A bell-shaped normal distribution graph is generated for normal and ischaemic ST segments. The results show average sensitivity (Se) 97.71% and positive predictivity (+P) 96.89% for 90 records of the annotated European ST-T database (EDB) after validation. These results are significantly better than those of the available methods reported in the literature. The simplicity and automatic discarding of irrelevant beats makes this method feasible for use in clinical systems.
Passive dynamic walking models are capable of capturing basic properties of walking behaviours and can generate stable human-like walking without any actuation on inclined surfaces. The passive compass gait model is among the simplest of such models, consisting of a planar point mass and two stick legs. A number of different actuation methods have been proposed both for this model and its more complex extensions to eliminate the need for a sloped ground, balancing collision losses using gravitational potential energy. In this study, we introduce and investigate an extended model with series-elastic actuation at the ankle towards a similar goal, realizing stable walking on level ground. Our model seeks to capture the basic structure of how humans utilize toe push-off prior to leg liftoff, and is intended to eventually be used for controlling the ankle joint in a lower-body robotic orthosis. We derive hybrid equations of motion for this model, and show numerically through Poincaré analysis that it can achieve asymptotically stable walking on level ground for certain choices of system parameters. We then study the bifurcation regimes of period doubling with this model, leading up to chaotic walking patterns. Finally, we show that feedback control on the initial extension of the series ankle spring can be used to improve and extend system stability.
Measurements of flow rates of fluids are important in industrial applications. Swirlmeters (vortex precession meters) are widely used in the natural gas industry because of their advantage in having a large measurement range and strong output signal. In this study, using air as a working medium, computational fluid dynamics (CFD) simulations of a swirlmeter were conducted using the Reynolds-averaged Navier–Stokes (RANS) and renormalization group (RNG) k– turbulence models. The internal flow characteristics and the influence of the tube structure (geometric parameter of flow passage) on metrological performance were studied, with a particular focus on the meter factor. Calibration experiments were performed to validate the CFD predictions; the results show good agreement with those from simulations. From the streamline distributions, a clear vortex precession is found in the throat region. At the end of throat, the pressure fluctuation reached a maximum accompanied by the largest shift in the vortex core from the centreline. There exists a large reverse flow zone in the vortex core region in the convergent section. To mitigate the influence of reverse flow on vortex precession, a suitable length of throat is required. For a larger convergent angle, the fluid undergoes higher acceleration leading to an increase in velocity that produces more intensive pressure fluctuations. The minor diameter of the throat also produces a higher velocity and larger meter factor. Compared with both divergent angle and throat length, the convergent angle and throat diameter play a more important role in determining precession frequency.
A novel algorithm, called the edge determination algorithm, for exact computation of the frequency response of a linear interval system is proposed. The algorithm formulates candidate curves for the frequency response boundaries as cubic Bezier curves. The edge determination algorithm operates on the cubic Bezier control points of these curves to obtain those, or their parts, that are on the frequency response boundaries. It presents the frequency response boundaries as an array whose entries are the cubic Bezier control points of the curves on the boundaries. Examples for two different cases are presented to illustrate the mechanics and validity of the algorithm.
Owing to errors made by the authors, Yuanzhe Wang and Hongjie Hu, the article is incorrect:
Wang Y, Hu H. Pseudo distributed optimal state estimation for a class of networked systems, Transactions of the Institute of Measurement and Control, 37 (10) 1232-1241, doi:
The authors apologise to the readers. The following corrections apply:
An acknowledgement should be included, as follows:
Dr Yuanzhe Wang completed the work for this paper while working with Prof. Tong Zhou’s group at Tsinghua University in 2013. For their contributions to the research behind this article, Dr Wang would like to acknowledge Professor Tong Zhou and his group.
Designing an adequate controller for a plant with an arbitrary relative degree is still an active area of research. In this paper, a discrete variable structure model reference adaptive control using only input-output measurements (DVS-MRAC-IO) for not strictly positive real systems with a relative degree of two is proposed. In order to show the effectiveness of the proposed controller, a detailed stability analysis is studied using Lyapunov theory. Further, a straightforward generalization of DVS-MRAC-IO for systems with arbitrary relative degree is presented. Numerical results are used to show the effectiveness of the proposed methods.
In this paper, the position tracking control problem of pneumatic servo systems is investigated. These systems usually have high nonlinearities and unmeasurable piston velocities. Firstly, by using adding a power integrator technique, a global finite-time state feedback controller is proposed. Secondly, based on homogeneous theory, a nonlinear observer is developed to estimate the piston velocity. Finally, the corresponding output feedback controller is derived, which local finite-time stabilizes the position tracking error system. Compared with the conventional backstepping output feedback control scheme, the developed nonsmooth output feedback control scheme offers a faster convergence rate and a better disturbance rejection property. Numerical simulations illustrate the effectiveness of the proposed control scheme.
This paper introduces an adaptive control method for finite-time modified function projective lag synchronization of uncertain hyperchaotic systems. Based upon novel nonsingular terminal sliding mode surfaces and the adaptive super-twisting algorithm, a controller is proposed to provide robustness, high precision and fast and finite-time modified function projective lag synchronization without the knowledge of the upper bound of uncertainties and unknown external disturbances. In addition, chattering is significantly attenuated due to the inherited continuity of the proposed controller. The global stability and finite-time convergence are rigorously proven. Numerical simulation is presented to demonstrate the effectiveness and feasibility of the proposed strategy and to verify the theoretical results.
In this paper, we present an enhanced coupling nonlinear control method for three-dimensional overhead crane systems under initial input constraints. The proposed control method can achieve superior control performance and strong robustness with respect to system parameter variations and external disturbances. Moreover, it guarantees ‘soft’ trolley start by introducing hyperbolic tangent functions into the controller. More precisely, we enhance the coupling behaviour between the trolley movement and the payload swing by fabricating two composite signals, based on which an energy-like storage function is established. Then, a nonlinear coupling control method under initial input constraints is derived directly. Lyapunov techniques and LaSalle’s invariance theorem are successfully adopted to find the asymptotic stability solution while satisfying the initial input constraints. Strict mathematical analysis of the control scheme with initial input constraints is provided as theoretical support for the superior performance of the controller. Simulation and experimental results are conducted to show the superior performance and strong robustness of the proposed control method.
Beam pumping units are the main production modes in the oilfield oil recovery process. As the power-driven equipment is driven by larger electrical power of the pumping motor, the high energy cost problem is widespread and hard to solve. The motor working efficiency is difficult to accurately match to the complex dynamic loads, as the relationship between the motor driven torque and dynamic loads is hard to master precisely, and the accurate calculation of some important variables cannot be effectively solved by the traditional mechanism modelling method. To solve this problem, in this paper, the dynamic changes of the polished rod loads are considered as the dynamic load changes of the beam pumping units and a serial hybrid model for the motor load torque is studied. Firstly, according to the working principles of the beam pumping units and the mechanism models of the pumping motor, belt pulley-gear box and four-bar linkage mechanism are respectively built. Secondly, for the underground frictions, a rough mechanism model based on the energy conservation equation is built first and then a data-driven soft sensor model is adopted to obtain the dynamic fluid levels instead of an off-line measurement, which uses a C index based on the on-line verification method to prevent the over-fitting in data-driven modelling process. Through the research of this article, a serial hybrid model is proposed to build the dynamic relationship between the pumping motor load torque and the polished rod loads, the underground frictions can be taken as a dynamic variable related to the time and the down-hole dynamic fluid level can be obtained by an on-line calculation. The proposed hybrid modelling method is applied to four normal oil wells, and comparisons between the simulation results and the actual test results verify its effectiveness.
This paper presents an optimal integral sliding mode control method based on a pseudospectral method for a class of affine systems with state and control constraints. First, a general form of an integral sliding mode is presented. Integral sliding mode control cannot deal with the problem of states and control constraints, nor can it satisfy the minimization of the cost function. The pseudospectral method has a high convergence speed and performs well in solving optimal control problems with general performance index, endpoint conditions and path constraints. In consideration of these advantages, an optimal integral sliding mode controller is determined by the pseudospectral method. Then, the stability analysis of optimal pseudospectral sliding mode method is discussed. Finally, numerical simulations show the effectiveness of the proposed method. An application example, consisting of an overhead crane system, is investigated to demonstrate the effectiveness and robustness of the proposed technique.
This article describes an investigation of a boundary control for vibration suppression of an axially moving accelerated or decelerated belt system with input saturation. Firstly, after considering the effects of the high acceleration or deceleration and unknown distributed disturbance, an infinite-dimensional model of the belt system is described by a nonhomogeneous partial differential equation and a set of ordinary differential equations. Secondly, by synthesizing boundary control techniques and Lyapunov’s direct method, a boundary control is developed to suppress the belt’s vibration and to stabilize the belt system at its equilibrium position globally; an auxiliary system is proposed to compensate for the nonlinear input saturation characteristic; a disturbance adaptation law is employed to mitigate the effects of unknown boundary disturbance; and the S-curve acceleration/deceleration method is adopted to plan the belt’s axial speed. Thirdly, with the proposed boundary control, the wellposedness of the closed-loop belt system is mathematically demonstrated and uniformly bounded stability of the closed-loop system is achieved without any discretization of the system dynamic model. Finally, simulation results are presented to verify the validity and effectiveness of the proposed control scheme.
A twin rotor multi-input multi-output system (TRMMS) is a high-order nonlinear system with a significant cross-coupling effect. The control of TRMMSs is considered a markedly challenging topic in the field of robust control. This study proposes a novel feedback linearization and feedforward neural network controller design for a TRMMS with almost disturbance decoupling (ADD) capabilities. The proposed composite controller achieves exponentially global stability and ADD performance without applying any traditional parallel learning algorithms. This study proposes an organization of the feedforward neural network and the weights among the layers to guarantee the stability of the overall system. A number of nonlinear systems, which are too complex to be solved by general ADD studies, are proposed in this study to demonstrate that the proposed methodology can effectively achieve the tracking and ADD performances through Matlab. Moreover, an efficient algorithm is proposed for designing the feedback linearization and feedforward neural network control with ADD and tracking capabilities.
Over the past two decades, the development of supervisory controllers that guarantee deadlock-free operation for automated manufacturing systems (AMSs) has been an active area of research. Most work to date assumes that the system resources are reliable. This paper focuses on the robust supervisory control problem of AMSs with a single unreliable resource. Our objective is to develop a robust supervisory control policy under which the system can continue producing in the face of the unreliable resource’s failure or recovery. To do so, we integrate an optimal deadlock avoidance policy based on a Petri net with a modified Banker’s Algorithm and present a novel robust supervisory control policy. It is proven to be of polynomial complexity and more permissive than two existing policies. Also, experimental results on a set of AMSs generated randomly indicate its superiority over all other existing policies.
In this paper, a model predictive control algorithm is presented for linear parameter varying systems with both state delays and randomly occurring input saturation. The input saturation is assumed to be occurred randomly with Bernoulli-distributed white sequences. A constant sate feedback law is designed at each time instant to ensure the robust stability of the closed-loop system with respect to polytopic uncertainties. The optimization of model predictive controller is cast into solving a linear matrix inequalities optimization problem. Then, the results are extended to gain-scheduled approach in which a set of state feedback laws are designed for each vertex of the system model. The state feedback law is scheduled by the time varying model parameters to achieve less conservatism in controller design. Finally, two examples are employed to show the effectiveness of the proposed algorithms.
In a magnetically suspended inertially stabilized platform, the yaw gimbal is suspended by the magnetic bearing, which can effectively isolate the external vibrations and disturbances. However, coupling torques and disturbance torques among gimbals still exist. Therefore, based on the cross feedback compensation, the output angles of gimbals are introduced as feedback variables, and the inverse coordinate transformation matrix is designed to compensate for the coupling torques. Furthermore, a disturbance observer is applied to inhibit the disturbance torque and simulations indicate that the disturbance observer can accurately estimate the disturbance torque. Consequently, the experimental results demonstrate that the cross feedback compensation can inhabit the coupling torques, and the disturbance observer greatly suppresses the external disturbance torques and improves the angular displacement precision of gimbals.
This paper studies sliding-mode control of a class of multibody underactuated systems with discontinuous friction on the unactuated configuration variable taking into account parametric uncertainties. Global motion for this class system including sticking, stick-slip, and slip regimes are analysed, and their corresponding equilibria are identified. The control objective is to avoid the sticking and the stick-slip regimes while tracking a desired velocity in the slip regime. Three sliding-mode controllers which are robust to parametric uncertainties are proposed, and their stabilities are proved using the Lyapunov direct method. Two examples, a mass-spring-damping system and a drill-string system, are used to demonstrate the validity of the proposed controllers.
This paper presents a fresh approach to the design of state observers for a class of time-delay systems with one time delay in the state and output vectors. By proposing a new coordinate state transformation, the system is first transformed into the new coordinates where all the delay terms associated with the state variables are injected into the system’s output and input. Thus, in the new coordinate system, a Luenberger-type state observer can be easily designed. Then, a backward state transformation problem is studied which allows us to reconstruct the original state vector of the system. Conditions for the existence of the state transformations and an algorithm for computing them are provided in this paper. We show that our approach works for a wider class of time-delay systems in the sense that when some existing state observer design methods fail to reconstruct the state vector, the proposed new change of coordinates and the observer scheme in this paper can still reconstruct the original state vector. Numerical examples and simulation results are given to illustrative the effectiveness of the proposed design approach.
In this paper a vision-based tracking controller is designed for the quadrotor vertical take-off and landing of an unmanned aerial vehicle. An imaged-based visual servoing approach is utilised to localise the quadrotor with respect to a moving target. Perspective image moments are used to define the visual features, which are projected on a rotated image plane to simplify the image dynamics. Attitude information and angular velocities are assumed to be available and the controller uses the flow of image features as the linear velocity cue. Presence of delay in processing and communication is modelled as a constant time delay in the force input of the translational dynamics, where a controller is designed for theses dynamics to compensate the delay effect. This controller is saturated in order to meet the quadrotor model constraint. A dynamic surface control approach is utilised for the rotational dynamics to track the desired attitude, defined through the position control loop. The stability properties of the complete control scheme are analysed using a theory of nonlinear cascaded systems. Simulation examples are provided in both nominal and perturbed conditions which show the effectiveness of the proposed theoretical results.
This paper investigates the problem of modelling and stabilization for a wireless based network control system with time delay. A model for the discrete-time system with time-varying delay is established to describe the system, and a static controller is designed that takes the feedback from both state and output into account. Based on Lyapunov stability theory and the linear matrix inequalities method, a new criterion is presented for stabilizing the discrete-time system with time-varying delay, and the corresponding controller parameter is obtained. A numerical example is given to demonstrate the effectiveness of the proposed approach.
In this paper, we investigate the problem of consensus tracking of a desired trajectory for a class of nonlinear systems consisting of multiple nonlinear subsystems with intrinsic mismatched unknown parameters. The subsystems are allowed to have non-identical dynamics with similar structures and the same yet arbitrary system order. Suppose that the communications among the subsystems can be represented by a directed graph. A fully distributed adaptive control approach based on a backstepping technique that is different from most of the existing results is proposed, which does not use global information as a parameter of the topology. It is proved that boundedness of all closed-loop signals and asymptotic consensus tracking for all the subsystems’ outputs are ensured. In simulation studies, a numerical example is illustrated to show the effectiveness of the control scheme. Moreover, the design strategy is successfully applied to solve a formation control problem for multiple underactuated ships.
This paper presents a comparative study between a sliding mode controller and a fractional order sliding mode controller applied to the problem of trajectory control of a ball in a ball and plate system. The ball and plate system is a well-known benchmark to test advanced control strategies because of its multivariable nonlinear coupled dynamics, open loop instability, parameter uncertainty, and under actuation. A cascaded sliding mode controller is initially designed to mitigate the problem. Furthermore, to improve the performance, a cascaded fractional order sliding mode controller is proposed. The proposed control strategies are experimentally validated on a ball and plate laboratory setup (Feedback Instruments Model No. 033-240). Simulation and experimental studies reveal that the fractional order sliding mode controller outperforms the sliding mode controller in terms of tracking accuracy, speed of response, chattering effect, and energy efficiency.
In this paper, we present a solution to the problem of non-fragile robust optimal guaranteed cost control for a class of uncertain two-dimensional(2-D) discrete systems described by the general model (GM) subject to both state and input delays. The parameter uncertainties are assumed norm-bounded. A linear matrix inequality (LMI)-based sufficient condition for the existence of non-fragile robust guaranteed cost controller is established. Furthermore, a convex optimization problem with LMI constraints is proposed to select a non-fragile robust optimal guaranteed cost controller stabilizing the uncertain 2-D discrete system with both state and input delays as well as achieving the least guaranteed cost for the resulting closed-loop system. The effectiveness of the proposed method is demonstrated with an illustrative example.
As a prevailing solar energy utilization equipment, the three-phase grid-connected photovoltaic (PV) inverter is widely operated in partially shaded conditions and thus tends to generate multiple local maximum power points on its power-to-voltage and current-to-voltage characteristic curves. In order to identify the global maximum power point (GMPP) quickly and precisely, this paper proposes a ripple-based maximum power point tracking method. It aims to perform the optimization of tracking using the segmented scanning of DC-side voltage. An improved adaptive perturb and observe (AP&O) method is introduced to maximize the solar conversion and to increase working stability. This method applies a hybrid model of fixed and variable step-size perturbation to restrain the fluctuation of PV-side voltage. It belongs to a two-stage GMPP tracking method. That is, when environmental factors such as irradiance and temperature change quickly PV power fluctuates sharply. Correspondingly, the AP&O method tracks the latest maximum power point (MPP) with a large fixed-step voltage reference command. When the PV power fluctuates smoothly under a slow environmental change rate, the algorithm applies multiple small and variable step-size voltage perturbations to vibrate round the location of GMPP. Simulation and experimental results show that this method improves the efficiency of the PV inverter tracking performance. In addition, the stability of DC bus voltage is guaranteed, and the operational stability of the photovoltaic power generation system is improved.
This paper investigates the problem of active disturbance attenuation control for a rotary inverted pendulum system. A nonlinear disturbance observer is first constructed to estimate the lumped disturbances, and then a feedback domination technique is integrated to handle the nonlinearities in a novel step-by-step way. Hence an exquisite composite controller can be constructed with strong robustness while the nominal control performance can be maintained. By utilizing a recursive stability analysis based on a Lyapunov function, the effectiveness of the proposed controller is assured. Numerical simulation results demonstrate the effectiveness of the proposed algorithm.
Human–robot interaction is inherently available and used actively in ankle rehabilitation robots. This interaction causes disturbances to be counteracted on the rehabilitation robots in order to reduce the side effects. This paper presents a fractional order proportional–integral–derivative controller to improve the trajectory tracking ability of a developed 2-degree of freedom parallel ankle rehabilitation robot subject to external disturbances. The parameters of the controller are optimally tuned by using both the cuckoo search algorithm and the particle swarm optimization algorithm. A traditional proportional–integral–derivative controller, which is also tuned using both of the algorithms, is designed to test the performance of the fractional order proportional–integral–derivative controller. The experimental results show that the optimally tuned FOPID controller improves the tracking performance of the ankle rehabilitation robot subject to external disturbances significantly and decreases the steady-state tracking errors compared to the optimally tuned PID controller.
The stability problem of nonlinear time-delay systems is addressed. A quadratic constraint is employed to exploit the structure of nonlinearity in dynamical systems via a set of multiplier matrices. This yields less conservative results concerning stability analysis. By employing a Wirtinger-based inequality, a delay-dependent stability criterion is derived in terms of linear matrix inequalities for the nominal and uncertain systems. A numerical example is used to demonstrate the effectiveness of the proposed stability conditions in dealing with some larger class of nonlinearities.
Traffic congestion is a common problem in merging regions of freeway networks. An adaptive integrated control method involving variable speed limits and ramp metering is presented with the aim of easing traffic congestion at merging regions. The problem of the imbalanced rights of ways of the upstream mainline and on-ramp at the merging region is solved by constructing the evaluation indices of congestion degree. Specifically, the traffic density and queue length of the upstream mainline and on-ramp are selected for use in the evaluation indices. Then, an adaptive controller is designed, integrating variable speed limits and ramp metering. The proposed method is tested in simulations considering a real freeway network in China calibrated by real traffic variables. The results show that the proposed adaptive integrated control method can prevent traffic flow breakdown and maintain a high outflow at the merging region during peak periods. The adaptive integrated control may lead to a 17% improvement in traffic delay.
This paper presents a robust control method combining the conventional proportional–integral–derivative (PID) scheme and the sliding mode fuzzy control scheme for a second-order non-linear system having uncertainties in the system dynamics. The goal of the proposed scheme is to force the response of the uncertain plant to follow that of the nominal model. The first phase of the design approach is to obtain a nominal PID controller for the nominal plant model. The poor performance of the sole PID scheme on the uncertain non-linear system motivates the proposal of the technique discussed here. To compensate for the deficiencies in the unit step response of the uncertain system, a fuzzy compensation scheme based on sliding mode control (SMC) is proposed and the PID loop is augmented by the proposed approach. It is shown that the performance with the proposed scheme is better than the sole PID-based control system. With the proposed technique, the response of the uncertain system converges to of the nominal system with admissible controller outputs. Furthermore, simulation results show that the proposed method produces consistent results even with noisy measurements.
A greenhouse system (GHS) is a closed structure that facilitates modified growth conditions to crops and provides protection from pests, diseases and adverse weather. However, a GHS exhibits non-linearity due to the interaction between the biological subsystem and the physical subsystem. Non-linear systems are difficult to control, particularly when their characteristics change with time. These systems are best handled with methods of computation intelligence, such as artificial neural networks (ANNs) and fuzzy systems. In the present work, the approximation capability of a neural network is used to model and control sufficient growth conditions of a GHS. An optimal neural network-based non-linear auto regressive with exogenous input (NARX) time series model is developed for a GHS. Based on the NARX model, two intelligent control schemes, namely a neural predictive controller (NPC) and non-linear auto regressive moving average (NARMA-L2) controller are proposed to achieve the desired growth conditions such as humidity and temperature for a better yield. Finally, closed-loop performances of the above two control schemes for servo and regulatory operations are analysed for various operating conditions using performance indices.
This paper investigates the problem of the high-performance motion loading control of an electro-hydraulic load simulator (EHLS). To begin with, the non-linear motion loading model of the EHLS was developed, by which the external disturbances caused by actuator active motion and the uncertainties arising from the EHLS were comprehensively considered. To address these uncertainties and disturbances, the adaptive robust torque control algorithm was developed with the motion loading model. In contrast to the available control methods concerning the EHLS, the developed method has two advantages. First, instead of performing the fixed coefficients-based linear feed-forward compensation using the actuator velocity, adaptive non-linear feed-forward compensation is achieved through the back-stepping design procedure. Second, the uncertainties and disturbances including system parametric uncertainties, un-modelled friction dynamics and unknown external disturbance are addressed comprehensively. Besides illustrating the theoretical proof, the effectiveness of the proposed method is verified through comparative experiments.
Driver inattention, either driver drowsiness or distraction, is a major contributor to serious traffic crashes. In general, most research on this topic studies driver drowsiness and distraction separately, and is often conducted in a well-controlled, simulated environment. By considering the reliability and flexibility of real-time driver monitoring systems, it is possible to evaluate driver inattention by the fusion of multiple selected cues in real life scenarios. This paper presents a real-time, visual-cue-based driver monitoring system, which can track both multi-level driver drowsiness and distraction simultaneously. A set of visual cues are adopted via analysis of drivers’ physical behaviour and driving performance. Driver drowsiness is evaluated using a multi-level scale, by applying evidence theory. Additionally, a general framework of extensive hierarchical combinations is used to generate a probabilistic evaluation of driving risk in real time. This driver inattention monitoring system with multimodal fusion has been proven to improve the accuracy of risk evaluation and reduce the rate of false alarms, and acceptance of the system is recommended.
Acknowledgement of renewable sources of energy as substitute energy sources for power production has expanded the number of distributed generation plants being incorporated into the conventional power distribution system. The single-phase voltage source inverter allying the photovoltaic plant with the grid has to address various issues identified with the quality of current injected into the grid, output power factor and power exchange between the plant and the grid. This paper concentrates on the investigation, design and implementation of a digital predictive current control technique known as the model predictive current controller for the control of single-phase photovoltaic distributed generation plants. The performance of the controller is evaluated under varied operating conditions. The proposed current controller is compared with the conventional proportional–integral controller in terms of its design methodology, steady state and dynamic response. The simulation and experimental results validates the effectiveness of the proposed model predictive current controller.
This paper aims to deal with the problem of fault detection in a closed-loop mode for a three-axis gyro-stabilized camera mount under the consideration of unknown disturbances. First, the influences of potential actuator and sensor faults are analysed and, based on this, the faults model as well as an equivalent additive fault input are introduced. For the purpose of fault detection, an observer-based fault detection filter is considered to generate the residual. The sensitivity of the residual to fault is evaluated by the non-zero singular values of the transfer function from fault to residual, whereas the robustness to disturbance is represented by the H-norm of the transfer function from disturbance to residual. To obtain a maximum sensitivity/robustness ratio criterion, the design of fault detection filter is formulated as an Hi/H optimization problem and the unified solutions for the optimization criterion are given. For residual evaluation, a window evaluation function is developed and an analysis of threshold selection is given to reduce the conservativeness of fault detection. Finally, experimental results are presented to demonstrate the effectiveness of the proposed method. It is demonstrated that the proposed method can effectively detect the provided fault as soon as possible.
In this paper, a new approach is recommended for damping out power system oscillations. In this approach, a new fuzzy neural proportional–integral (PI) controller (FNPIC) based static synchronous series compensator (SSSC) and a fuzzy-power system stabilizer (fuzzy-PSS) with a new structure are simultaneously employed. Adaptive learning rates based on the Lyapunov stability theory to enhance the convergence speed of the proposed controller are obtained. In the structure of fuzzy-PSS, a pattern search algorithm is used to adjust four gains. A two-area four-machine power system and a three-area, three-machine power system are employed to investigate the efficiency of the proposed method. Simulations results confirm the capabilities of proposed controller in suppressing the power system oscillations.
Two-phase flow widely exists in many industries. Understanding local characteristics of two-phase flow under different flow conditions in piping systems is important to design and optimize the industrial process for higher productivity and lower cost. Air–water two-phase flow experiments were conducted with a 16x16 conductivity wire-mesh sensor (WMS) in a horizontal pipe of a multiphase flow facility. The cross-sectional void fraction time series was analysed by the probability density function (PDF), which described the void fraction fluctuation at different flow conditions. The changes and causes of PDFs during a flow regime transition were analysed. The local structure and flow behaviour were characterized by the local flow spectrum energy analysis and the local void fraction distribution (horizontal, vertical and radial direction) analysis. Finally, three-dimensional transient flow fluctuation energy evolution and characteristic scale distribution based on wavelet analysis of air–water two-phase flow were presented, which revealed the structural features of each phase in two-phase flow.
Nowadays, transfer learning (TL) has become a crucial technique to accelerate the slow optimization procedure of reinforcement learning (RL) by re-utilizing knowledge acquired in a previous related task. Nevertheless, most of the current relevant research acquires knowledge through RL training in the source task, which would be too time-consuming. In view of this situation, in this paper, we propose a novel TL framework where the agent extracts knowledge from human-demonstration trajectories of the source task and reuses the knowledge in RL in the target task. As for what to transfer, two forms of knowledge deduced from the demonstration trajectories, which are the k-nearest neighbour of the current state in source samples and visit frequency of homologous states, are adopted. For how to transfer, the two forms of knowledge are respectively used to recommend a preferred action when random exploration is needed and to shape an instantaneous reward for RL. Simulation experiments of balancing Cart-Poles with different difficulties suggest that both the two forms of knowledge accelerate the learning process of RL obviously. What is more, the effect is even more significant when they are used in combination. In this case, the experimental results manifest the positive role of our framework in RL.
This study presents numerical and experimental investigations targeting enhancing the magnetic field flux magnitudes in electroplated copper microcoils. Improved designs are used in the development of a new generation of the electromagnetic-based synchronous micropump in order to enhance its performance (i.e. the maximum achievable output pressure and flow rate). The synchronous micropump concept is based on managing the movement of two magnets in an annular fluidic channel. The magnets’ rotation is achieved by sequentially activating a set of three-dimensional microcoils to repel or attract one magnet (travelling piston) through the channel, whereas the second one is anchored between the inlet and the outlet ports (valve piston). At the end of each pumping cycle, the magnets exchange their functions. To achieve the maximum achievable output pressure and flow rate, higher magnetic fields without exceeding the material temperature limitation are required. The stronger the magnetic fields that can be generated, the higher the hydraulic power that the pump can deliver. The microcoil conductor width and height were optimized to generate higher magnetic field flux intensity within the pump limitation parameters (i.e. pump footprint and coils’ maximum heat dissipation limit). The new generation of the synchronous pump was run at a rotational speed of up to 800 rpm and provided a maximum flow rate of 3.56 ml/min and a maximum pressure head of 687 Pa.
In this paper we propose a phase-constrained fractional order
The observer design for partial differential equations has so far been an open problem. In this paper, an observer design for systems with distributed parameters using sliding modes theory and backstepping-like procedure in order to achieve exponential convergence is presented. Such an observer is built using the knowledge available within and throughout an integral transformation of Volterra with the output injection functions. The gains of the observer, which are attained by solving a partial differential equations system with output injection, will guarantee the exponential convergence of the observer. The design method is applied to an epidemic system to consider the sensitive population S.
This paper presents a novel equivalent control-based adaptive sliding mode control (EASMC) approach for designing the autopilot of a bank-to-turn (BTT) missile under model uncertainties and external disturbances. The sliding surface is constructed with a tracking error between the real attitude angle and the reference command. The equivalent control technique works as a mechanism for the gains over-bounded by uncertainties and this information is implemented in the adaption progress. The method guarantees that the sliding surface reaches zero in finite time and the error tracks the command value asymptotically. The advantage of this method is that the gains will be adapted to counteract uncertainties and enable the control deflection magnitude to be reduced to the minimum value, keeping the property of a finite-time convergence. The skill in choosing the gains is also given in this paper. Simulation results demonstrate that the approach proposed is able to improve the dynamic performance and robustness of a BTT missile system.
This paper demonstrates a multi-input multi-output (MIMO) robust control approach where multiple scheduled designs are merged to produce a smooth control law. The design is verified using software-in-the-loop (SIL) testing based on blade element theory (BET) for highly realistic flight simulations. An inner-loop attitude controller balances performance and robustness, achieving a fast response time, low overshoot, good noise rejection and minimal lateral–longitudinal coupling. The controllers are formed at several predetermined grid points so the design covers a wide flight envelope. Blade element SIL testing shows that the flight control system preserves stable flight and follows the references well, even under tough weather conditions. The proposed strategy is also compared with a classical autopilot design procedure and is seen to be superior.
Multi-agent consensus has been widely applied in engineering. A novel protocol that can achieve an average state consensus for multi-agent systems in finite time is presented in this paper. The proposed protocol contains a non-linear and a linear term. The state consensus is achieved in finite time by the non-linear term and convergence performance is improved by the linear term to some degree. The protocol can be applied to systems with a switching topology as long as the communication graph is always undirected and connected. The upper bound of convergence time is obtained. The relationship between convergence time and protocol parameter, communication topology and initial state is analysed. Lastly, simulations are conducted to verify the effectiveness of the results.
This paper presents a method for online identification of non-linear dynamic systems using the Wiener model. For the linear dynamic part the subspace identification method with the multivariable output-error state-space algorithm is employed, whereas for the non-linear static part the multi-layer perceptron neural network with Levenberg–Marquardt algorithm is used. The stability and convergence of the proposed method is shown using the Lyapunov direct method and the region solution of the linear matrix inequality (LMI) approach. The proposed method is tested by simulations performed on the continuous stirred tank reactor (CSTR) plant, which is presented by non-linear differential equations. Moreover, the method is applied on the input–output data that are obtained from a practical system of the CSTR plant as well as the pH neutralization plant. The results show significant improvements in online identification of the non-linear dynamic systems compared with the recently reported methods in literature.
In this paper, after complete modelling of a flexible satellite equipped with a control moment gyroscope (CMG) actuator, it is shown that a PD-like controller can globally asymptotically stabilize this satellite by using Lyapunov’s direct method. Despite the simplicity, simulations show that the controller can stabilize the flexible satellite in a three-axis manoeuvre even in the presence of external disturbances. Then, using a non-linear variable gains PD controller, which only uses angular velocity of the rigid body and the attitude parameters as the inputs, the performance of the control system is improved in some important aspects such as reducing maximum control torque, reducing maximum peak of deflection of the appendages and increasing robustness of the controller against the orbital disturbances. In addition, locally asymptotically stability of the non-linear variable gain PD controller is guaranteed using a novel Lyapunov candidate function. Considering the difficulty in measuring the appendages’ deflection and the primarily existence of parameter uncertainties, and as this controller is independent of changes in these parameters, such a control system is very useful and applicable. In order to validate the system’s mathematical model and the control system performance, an exact model of the satellite is constructed in the ADAMS/View software that is linked to the MATLAB software. The efficacy of the proposed approach is demonstrated by several numerical examples.
For a bearingless motor that has two sets of stator windings, i.e. torque windings and suspension windings, the mutual inductance between the two sets of stator windings is a critical parameter; it is the basis of displacement sensorless control technology. Aiming at a three-phase bearingless motor, the equivalent two-phase mutual inductance model between two sets of stator windings is deduced. Then, based on the mutual inductance measurement method of a simple two-phase bearingless motor, a novel measurement algorithm of the equivalent two-phase mutual inductance is proposed, and the proposed measurement algorithm is applicable for a three-phase bearingless motor. Finally, based on a three-phase bearingless induction prototype motor, the experimental measurement of the equivalent two-phase mutual inductance is carried out. From the experimental results, it is clear that within the limited radial eccentricity of the rotor, the measured value of the equivalent two-phase mutual inductance is approximately proportional to the radial displacement of the rotor, and the correction coefficient kc of the equivalent two-phase mutual inductance model is derived; the consistency between measured and calculated values has verified the validities of the equivalent two-phase mutual inductance model and the proposed measurement algorithm from one instance. Thus, the theory foundation has been laid for research on the displacement sensorless vector control technology of a three-phase bearingless motor.
In this article, the Meixner-like model is used to represent the linear discrete-time system in comparison with the Laguerre model. Furthermore, we propose from input/output measurements a new recursive representation of the Meixner-like model. However, a significant approximation of this model is subject to an optimal choice of Meixner-like parameters: pole, order of generalization and Fourier coefficients. The present research yields a series of computational experiments, through a simulation example of a linear parameter varying (LPV) system. This study tests this approach, verifies the theoretical results and points out the positive outcomes of using the Meixner-like model in comparison with the Laguerre model.
A novel fibre surface plasmon resonance (SPR) sensor fabricated by a silver mirror reaction is first proposed and demonstrated in this paper. The experimental results showed that the silver film characteristics of the fibre SPR sensing probe are affected by the concentration of silver ammonia solution, and the relations between the concentration of silver ammonia solution and properties of the sensors have been obtained, which is in accordance with the optimal parameters of fibre and silver film through a theoretical simulation. Firstly, a theoretical model of a silver film-based fibre SPR sensing mechanism has been built up. Then the numerical simulations towards the influence of sensing structure, thickness and sensing length of metal films on the sensing system sensitivity have been performed. Finally, the optimal structure parameters of the sensor are obtained. The results show that this fibre SPR sensing system provides a promising platform for sodium chloride solution concentration measurement with a concentration sensitivity of 710.4 nm/%.
We provide the complete design of a hybrid exponentially weighted moving average (HEWMA) control chart for COM-Poisson distribution. The necessary measures of the proposed control chart are given in this manuscript, and the average run lengths (ARLs) are determined through Monte Carlo simulation for various values of specified parameters. The performance of the proposed chart is compared with two existing control charts. The proposed chart is more efficient than these two existing charts in terms of ARLs; application of the proposed chart is described with the help of Montgomery’s data (Introduction to Statistical Quality Control, John Wiley & Sons, New York, 2007).
In this study, the displacement and blocking force of the tip point of a cantilevered electro-active polymer (EAP) actuator has been controlled for a cell injection process which consists of approaching, interacting and leaving steps. A vision-based system is used to acquire the tip displacement data for identifying a transfer function model of the actuator and its position control. Discrete time Proportional-Integral controllers are used to control the position and blocking force. A Smith Predictor is utilized in the vision-based position control system to compensate for the time delay due to image processing. Experimental position and blocking force results prove that the proposed control strategies are effective enough to guide the actuator to undertake the cell injection process. This study contributes to the previously published work from the point of view of simultaneously controlling the position and blocking force of the electroactive polymer actuators and widening their application areas.
Chen W, Liu T, Li W, Wang J, Wu X and Liu D, Locomotion control with sensor-driven reflex for a hexapod robot walking on uneven terrain. Transactions of the Institute of Measurement and Control 38(8): 956–970, doi:
In this paper a new type of sliding mode based fractional-order iterative learning control (ILC) is proposed for nonlinear systems in the presence of uncertainties. For the first time, a sliding mode controller is combined with fractional-order ILC. This sliding mode based
Owing to errors made by the authors, Caiqin Song and Jun-e Feng, the article is incorrect:
Song C and Feng J An iterative algorithm to solve the generalized coupled Sylvester-transpose matrix equations. Transactions of the Institute of Measurement and Control 38(7): 863–875, doi:
An adaptive prescribed performance sliding mode control (APPSMC) of Micro-Electro-Mechanical System gyroscopes is proposed for the trajectory tracking in the presence of parameter variations and external disturbances. Steady-state error, transient error and convergence rate are important performance indexes in gyroscope systems. However, these indexes have not been investigated and corresponding control methods are not investigated as well. The proposed APPSMC scheme can guarantee that the tracking error is strictly within a predefined performance bound and the convergence rate is no less than a predefined value. All the gyroscope parameters including the angular velocity can be correctly estimated by adaptive laws and the disturbance bound is estimated by a neural network estimator to alleviate the chattering problem. Simulation results demonstrate the effectiveness of the proposed adaptive prescribed performance sliding mode controller.
The problem of station-keeping attitude tracking control for an autonomous airship with system uncertainties and external disturbances is investigated. Adaptive laws are applied to estimate the upper bounds of uncertainties and disturbances, and a nonlinear finite time control scheme is proposed by combing input/output feedback linearization with integral sliding mode technique. Different from the existing works on attitude control of airship, the developed controller can guarantee the yaw, pitch and roll angle trajectories track the desired attitude in finite time in spite of uncertain system uncertainties and external disturbances. Simulation results are provided to illustrate the attitude tracking performance.
Yearly preventive maintenance scheduling of generating units in a restructured power system is one of the most important problems that have to be solved in modern power systems. In this paper, a bilevel approach is used for modelling of the preventive maintenance scheduling problem. The upper level of this bilevel problem represents the revenue function of power units owned by a generation company (GENCO), whereas the lower-level problem represents the market-clearing process and is usually called the independent system operator (ISO) level. This bilevel problem is then formulated as a mathematical program with equilibrium constraints (MPEC) using the primal–dual theorem, which converts the problem into a single-level mixed-integer non-linear optimization problem that can be solved using programming software. Various case studies are conducted using the IEEE reliability test system (RTS) and the obtained results are compared.
The inherent non-linear factors and the interference of external surplus torque of the electric load simulator make it difficult for the conventional control methods to achieve satisfactory control effect. The cerebellar model articulation controller (CMAC) is widely used because of its simple structure and quick learning. However, conventional CMAC utilizes binary 0 and 1 logic, which will lead to the divergence during the torque tracking. Inspired by the cognitive characteristics of the human brain, the fuzzy logic is implemented to CMAC. In this paper, we design a naive method of applying fuzzy logic to CMAC (NFCMAC), which can take both advantages of the local neural network and the global neural network, and present a new interpretation of the entire network. With parallel control of a PD controller, the NFCMAC–PD control strategy has been successfully applied to the electric load simulator. Dynamic simulation and experimental results have indicated that the NFCMAC–PD control strategy can ensure the control precision, restrain interference and be free of divergence, while satisfy the control requirement of the passive loading system.
Semi-supervised learning aims to utilize both labelled and unlabelled data to improve learning performance. This paper shows a distinct way to exploit unlabelled data for traditional semi-supervised learning methods, such as self-training. Self-training is a well-known semi-supervised learning algorithm which iteratively trains a classifier by bootstrapping from unlabelled data. Standard self-training barely selects unlabelled examples for training set augmentation according to the current classifier model, which is trained only on the labelled data. This could be problematic since the underlying classifier is not strong enough, especially when initial labelled data is sparse. Consequently, self-training suffers from too much classification noise accumulated in the training set. In this paper, we propose a novel self-training style algorithm, which exploits a manifold assumption to optimize the self-labelling process. Unlike standard self-training, our algorithm utilizes labelled and unlabelled data as a whole to label and select unlabelled examples for training set augmentation. In detail, two measures are employed to minimize the effect of noise introduced to the labelled training set: a transductive method based on controlled graph random walk is incorporated to generate reliable predictions on unlabelled data; secondly, the mechanism is adopted to sequentially augment the training set. Empirical results suggest that the proposed method can effectively improve classification performance.
This paper presents a new approach for guaranteed state estimation based on zonotopes and ellipsoids for linear discrete-time systems with unknown but bounded perturbations and noises. At each sample time, a predicted state set is calculated by zonotopes and an outer bounding ellipsoid of the predicted state set is calculated. State estimation is calculated directly by intersects with an outer bounding ellipsoid and strip. The precision of the state estimation will increase as output order improves by this method. Two examples have been provided to clarify the algorithm.
This paper considers the adaptive state-feedback control problem for a class of high-order non-linear systems with unknown control coefficient and time delays. By applying the neural network approximation method and the Nussbaum function approach, the restrictions on non-linear functions and the conditions on the time-varying control coefficient are largely relaxed. In addition, an adaptive neural network state-feedback controller with only one adaptive parameter is successfully constructed by introducing proper Lyapunov–Krasovskii functionals and using the backstepping technique. The proposed scheme guarantees the closed-loop system to be semi-globally uniformly ultimately bounded. Finally, a simulation example demonstrates the effectiveness of the controller.
The stabilizing decentralized controller design problem for (possibly descriptor-type) linear time-invariant neutral time-delay systems is considered. A design approach, based on the continuous pole placement algorithm and the decentralized pole assignment algorithm, is proposed. A design example is also presented, to demonstrate the proposed approach.
In this paper we present an extension of three important model reduction techniques: namely, the stability equation, the modified pole clustering and the dominant modes methods for conventional (regular) systems to reduce complexity relating to high dimensionality of mathematical models representing physical, generalized (also called singular) systems. Combining these methods to Genetic Algorithms’ tools and exploiting a special representation base where a full order singular system is deflating into proper and improper subsystems, different natures of stable, optimal low order models are obtained. To show the effectiveness of the proposed algorithms, a numerical example is given, where six approximants are derived from a multi-input multi-output singular system. By the use of two optimal norms, the MOR errors are quantified and permits to conclude to the quality of the proposed reduced order models.
In this paper, a robust multiobserver is proposed for the state estimation of discrete-time uncertain nonlinear systems with time-varying delay. The designed multiobserver is based on the decoupled multimodel approach. Unlike the classically used multimodel structures, the decoupled multimodel provides a flexibility of modelling. Indeed, the partial models’ structures can be adapted to the complexity of the system in each operating regime, thus the partial models can be with different dimensions. Delay-dependent sufficient conditions for the synthesis of a robust multiobserver against norm-bounded parametric uncertainties and in the presence of measurement noise are established in terms of linear matrix inequalities. A simulation example is given to illustrate the effectiveness of the designed multiobserver.
In this paper, we present an accelerated gradient-based iterative algorithm for solving extended Sylvester–conjugate matrix equations. The idea is from the gradient-based method introduced in Wu et al. (Applied Mathematics and Computation 217(1): 130–142, 2010a) and the relaxed gradient-based algorithm proposed in Ramadan et al. (Asian Journal of Control 16(5): 1–8, 2014) and the modified gradient-based algorithm proposed in Bayoumi (PhD thesis, Ain Shams University, 2014). The convergence analysis of the algorithm is investigated. We show that the iterative solution converges to the exact solution for any initial value provided some appropriate assumptions be made. A numerical example is given to illustrate the effectiveness of the proposed method and to test its efficiency and accuracy compared with an existing one presented in Wu et al. (2010a), Ramadan et al. (2014) and Bayoumi (2014).
This paper discusses the global robust output regulation problem for a class of nonlinear output feedback systems. It is assumed that the exosystem and the high-frequency gain sign are unknown and that the unknown parameters can be arbitrarily large. To solve this problem, two major challenges are to be overcome. First, the concurrence of the unknown exosystem and the unknown high-frequency gain sign cannot be handled merely by designing estimators for the two unknown parameters respectively. Second, the conventional extended matching design approach cannot be directly implemented, owing to the arbitrarily large unknown parameters. To cope with these difficulties, a new estimator is developed, and the extended matching design approach is modified to obtain a suitable update law for the estimator. The effectiveness of the proposed adaptive controller is illustrated by an example.
In this paper, we used a Qball-X4 quad-rotor unmanned aerial vehicle (UAV) which was developed by the Quanser Company as the experimental platform. First, a fundamental mathematical model of the Qball-X4 quad-rotor UAV was built and a simulation model was set up based on the proposed mathematical model; then, a double closed-loop optimal proportional–integral–derivative (PID) controller based on integral of time multiplied by absolute error (ITAE) indices was designed according to the model structure. In consideration of the possible system error and data delay, we designed a corresponding Kalman filter, which can estimate the target trajectory and be put before the proposed PID controller to ensure their validity. Finally, simulation results of the system with presented PID controller and Kalman filter were shown to verify their effectiveness.
Owing to the dynamic operation mode of urea selective catalytic reduction (urea-SCR) systems, advanced control strategies are required to improve urea dosing control. A new control-oriented model presentation of urea-SCR systems is developed in this study. A novel controller based on the triple-step non-linear method is designed. The controller drives the non-linear system with time-varying parameters to track the variable ammonia coverage ratio. Unlike the existing triple-step non-linear method, the third design procedure in the proposed method is adjusted as an
The two-parameter generalized Hermitian and skew-Hermitian splitting (TGHSS) iteration method is applied to solve the continuous Sylvester equation
Robot manipulators have been successfully utilized in assembly lines within the last few decades. In order to increase productivity and diminish costs, they have been enrolled at several stages of automation, including transportation, welding, mounting and quality control processes of the components that are assembled to construct the entire system. In this study, an unusual method is proposed to make the robot manipulators and moving belts serve accordingly in an efficient manner. To this extent, the motion of a two-link robot manipulator is planned in a continuous fashion by the use of a proper guidance law compatible with the uninterrupted movement of the moving belt upon which the components are placed by means of the manipulator. For this purpose, a control system is built for the manipulator based on its dynamic modelling by regarding the PI (proportional plus integral) control law in accordance with the linear homing guidance law. Moreover, engagement geometry is constructed. Having performed computer simulations, it is observed that the tip point of the manipulator can catch the slot on the belt at speeds from 0.5 to 2.5 m/s for different initial positions and speeds of the tip point from 5.0x10–5 to 0.5 m/s.
In this paper, we investigate the misleading effect of measurement errors on simultaneous monitoring of the multivariate process mean and variability. For this purpose, we incorporate the measurement errors into a hybrid method based on the generalized likelihood ratio (GLR) and exponentially weighted moving average (EWMA) control charts. After that, we propose four remedial methods to decrease the effects of measurement errors on the performance of the monitoring procedure. The performance of the monitoring procedure as well as the proposed remedial methods is investigated through extensive simulation studies and a real data example.
Currently, fractional-order systems are attracting the attention of many researchers because they present a better representation of many physical systems in several areas, compared with integer-order models. This article contains two main contributions. In the first one, we suggest a new approach to fractional-order systems modelling. This model is represented by an explicit transfer function based on the multi-model approach. In the second contribution, a new method of computation of the validity of library models, according to the frequency
This paper studies the H consensus problem of multi-agent systems with Lipschitz non-linearities and external disturbances in a general network. The topology is just required to contain a directed spanning tree. Distributed consensus controllers are constructed based on relative states information of neighbour agents. A novel matrix decomposition based approach is introduced to analyse the H consensus problem, in which the H consensus problem is converted into a H control problem of lower dimension system by performing a proper linear variable transformation. Finally, the effectiveness of the theoretical results is illustrated via a numerical simulation.
The trajectory tracking control problem of dynamic nonholonomic wheeled mobile robots is considered via visual servoing feedback. A novel visual feedback tracking error model is proposed. Its tracking controller is independent of uncalibrated visual parameters by using new methods. This controller consists of two units: one is an adaptive control for compensation of the uncertainties of dynamic parameters, the other is a variable structure control for the interference suppression. In addition, the torque tracking controller is global and smooth, and the chattering phenomenon is eliminated. The asymptotic convergence of tracking errors to equilibrium point is rigorously proved by the Lyapunov method. Simulation and experiment results are provided to illustrate the performance of the control law.
This article focuses on the robot workstation layout problem and briefly discusses a recovery control strategy. Since present industrial workstations utilize a flexible manufacturing cell served by a robot, researchers in this field try to find the best method determining the physical organization of resources in available space. As solving the facility layout problem (FLP) might reduce material handling expenses, the most common objective in these approaches is to minimize the material handling costs. Our work introduces a new approach in obtaining the optimal positions of resources in a robot workstation where considerable contribution to the final layout design comes from the failure recovery data. The optimization criteria include material flow and transportation cost as the standard FLP objectives. In our approach we also consider the resource rate of failure and treatment quality as a part of the failure recovery. The optimization problems were solved with the state of the art optimization algorithm for the nonlinear optimization problems. The computational results of the study are discussed and analysed on the basis of a real industrial application. The commonly used objective function is compared to the proposed objective function extended with the failure recovery. As an important part of the failure recovery strategy, making the proper recovery decision in the workstation control design is also discussed.
This paper proposes a method for separation of broken rotor bar failures from low-frequency load torque oscillation in direct torque control (DTC) induction motor drives by using vq voltage and iq current components’ spectra. The effect of load torque oscillation should be considered in induction motor drives for reliable broken bar fault detection. Induction machine drivers are run in DTC mode to control its torque and speed. In practice, the presence of load torque fluctuation may sometimes cause false positive alarms on stator current spectrum. However, discerning of broken rotor bar failure from low-frequency load variation for DTC drives remains unexplored. Experimental results show that by using the proposed method broken rotor bar failure can be reliably detected in the presence of low-frequency load torque oscillation in DTC induction motor drives.
In this paper, the normalized least mean square (NLMS) algorithm, a time-varying signal processing method, is employed in a Coriolis mass flowmeter (CFM) to improve its weak anti-jamming capability. Initially, the fundamental principles of the NLMS algorithm adopted in the adaptive filter are analysed. Then, the NLMS algorithm is applied to analyse the signal processing of the CFM at different flow rates in experiments. By comparing several performance indicators and spectrum diagrams from being filtered by the NLMS algorithm and the least mean square (LMS) algorithm, the results indicate that the NLMS algorithm can lead to a better anti-jamming capability and reduce the influence of noise efficiently for the CFM. In addition, the NLMS method has a faster convergence speed and fewer stable errors than the LMS method. Therefore, the NLMS can improve the quality of the output signal of the CFM.
A parking management system needs to be designed to improve the efficiency of the management of parking lots in urban business districts that witness difficult parking. In this paper, the overall design of an intelligent parking system is expounded, the architecture of which includes the PXA270 master control platform, ZigBee network, the remote communication section and Android software; the LCD interface circuit is stated and the design philosophy for the analogue camera circuit is described. Meanwhile, the functions of the parking-space detection module and the vehicle identification unit are explained, and the structure diagram of the software in the intelligent parking system is put forward. Also, the design philosophies for the gate control unit and the GPRS communication program are expounded, a detailed definition of the control frame of the gate module is given, the flow charts of the parking-space status detection procedure and the vehicle identification procedure are presented, and the server of the Android system and the software design for the client are described. Furthermore, the flow chart of the network communication thread is put forward, in which data is transferred between the server and the client, the interface design for the intelligent parking system is realized, the display of the system’s main interface is presented, and the parking-space reservation interface and the function of entry guiding animation are realized.
Automated, precise single particle manipulation in the microscale is in great demand and is one of the great challenges in biomedical and biochemical engineering. Automatic micromanipulation has also become a microrobotics challenge. Following this challenge, control technology is integrated with dielectrophoresis (DEP)-based micromanipulation technology in this paper to construct automatic DEP-based micromanipulation systems. DEP micromanipulation systems with electrodes of quadrupole polynomial geometry are developed as controllable microactuators. A semianalytical modelling method is proposed to formulate the analytical models of the DEP manipulation systems, which manifests that the DEP manipulation systems are non-affine non-linear systems. Then, taking the parameter uncertainties, unmodelled dynamics and external disturbances into account, an adaptive law combined with a dynamic sliding mode controller is designed for two-dimensional trajectory tracking control of a DEP micromanipulation system. The closed-loop system is proved stable in the presence of bounded lumped uncertainty based on the Lyapunov theorem. Finally, simulation results show the validity of the proposed control design.
This paper introduces a new hybrid controller for position and force control of an electrohydraulic car active suspension. In most hybrid controllers, a switching function is normally used in order to take advantage of two separate controllers. Switching between the two controllers produces a chattering in the system, in addition to the chattering that may be inherent to the controller itself, which deteriorates the system performance. In this work, we resolved the switching limitations dilemma by transitioning from one controller to another through two low-pass filters. These filters are used with variable gains to improve the new hybrid position/force controller performance that we developed. The produced control signal is a structured combination, in which the signal coming from the position controller reduces the effect of road perturbations on passengers by bringing the car’s vertical motion to zero. Simultaneously, the signal from the force controller tracks a reference force and thus reduces the force transmitted to passengers. To eliminate the chattering that is inherent to the sliding mode controller, we introduced an exponential reaching law function to the hybrid sliding mode controller. This exponential function also reduced the response time, consequently speeding up the system reaction to suppress perturbations. In addition to that, a recent sliding surface-based controller is applied to vary the filters’ gains and obtain better performance. The frequency analysis is done to verify the controller performance. The proposed hybrid controller is also validated in real time on active suspension workbench and compared with a classical PID controller.
Through-the-wall radar imaging (TWRI) applications allow accurate target localization and high-resolution imaging. However, multipath propagation generates challenges to the image reconstruction procedure. With distortions in the received radar signals, traditional imaging algorithms are not able to acquire high-resolution images. The unpredictability of the indoor scattering environment makes this even worse. In this paper, a novel block orthogonal matching pursuit (BOMP)-based group-sparsity reconstruction algorithm combined with particle swarm optimization (PSO) is proposed for reliable scene reconstruction. The proposed imaging algorithm can recover the image of targets by exploiting multipath propagation and simultaneously estimating wall parameters with high accuracy. The effectiveness of the proposed imaging method has been further demonstrated via simulation results.
In this paper, a new method for quantitative determination of fat content in goat milk at room temperature (25°C), based on the annular photoelectric sensor system, has been developed. The measurement system consists of an annular photoelectric sensor, thermoelectric cooler (TEC), amplifier, A/D converter, microprocessor and other components. Based on Mie theory, the diffuse reflection light intensity is adopted as the optical parameter representing the fat content in goat milk. In this way, the standard model of the system can be established and loaded into the processor to realize data acquisition, processing and output. The proposed method has been tested on goat milk samples with variable fat content in the prediction experiment. The results show that the fitting equation is y=–0.366x+0.295 (R2=0.987) and measurement errors are within ±3%, which indicates that the presented method has high prediction performance and can measure the fat content in goat milk accurately and in real time.
Based on the homogeneous domination approach and stochastic nonlinear time-delay system stability criterion, this paper investigates the global state-feedback stabilization problem for a class of stochastic high-order upper-triangular nonlinear systems with input time-varying delay. By skilfully choosing an appropriate Lyapunov–Krasoviskii functional and successfully solving several troublesome obstacles in the design and analysis procedure, a delay-independent state-feedback controller is designed to render the closed-loop system globally asymptotically stable in probability. The simulation example is given to verify the effectiveness of the proposed design scheme.
This paper presents a new strategy for tuning the coefficients of a PID controller regarding H robust performance and stability constraints based on the constrained artificial bee colony (CABC) algorithm. First, the issue of tuning the PID controller to follow H specifications are introduced, and the objective function and constraints of the optimization problem are specified. Then, a simple and efficient method of transforming the constrained optimization problem to an unconstrained one is presented, and used within the CABC for optimization. The CABC is one of the most recently introduced optimization algorithms, and has the advantages of strong robustness, fast convergence and high flexibility, with fewer setting parameters. The algorithm is essentially a random and intelligent evolutionary method. This method is also utilized and simulated in several models. The simulation results have been compared with other techniques, which demonstrate the efficiency and superiority of the proposed scheme.
A novel method for mass air flow (MAF) sensor bias compensation and error map (or look-up table) adaptation with model error correction is proposed. A key feature of the approach is its method of handling and storing operating-point-dependent MAF sensor errors due to installation and ageing in diesel engines; such errors lead to adverse impacts on emission performance. The model of the MAF sensor error depending on the engine operating point is represented as a two-dimensional (2D) map, which is described as a piecewise bilinear interpolation model in the form of a vector–vector dot product. The mean-value engine model of a diesel engine with additional model biases is analysed and employed to improve the estimation precision of the 2D map. Based on the combination of the 2D map regression model and diesel engine mean-value engine model with additional model biases, a linear parameter varying adaptive sliding mode observer is designed, which achieves the disturbance suppression for the nonlinear model errors, as well as the simultaneous estimation of the system state, linear model errors and map parameters. The convergence of the proposed algorithm is proven under the conditions of the persistent excitation and given inequalities. The observer is validated against simulation data from the engine software enDYNA provided by TESIS. The results demonstrate that the estimation precision of the MAF sensor error map can be improved using the proposed method.
The steady and reliable operation of a ship’s diesel engine is important to the ship’s electrical power system and the engine’s performance, and stable control of rotational speed is crucial to a diesel engine’s emission, economy and power performance. A ship’s diesel engine is a nonlinear and time-varying system. A traditional proportional–integral–derivative (PID) controller cannot regulate the speed under different working conditions. In this paper, a nonlinear mathematical model for speed regulation of diesel engines is established according to experiments and a multi-sliding surface variable structure controller for speed regulation of diesel engine is established by sliding mode control. A bulk cargo ship 500-I was analysed as an example. The MATLAB/Simulink simulation took the navigation environment and the effect of the ship propeller on the diesel engine into consideration. A simulation model considering the whole ship–engine–propeller system is built and some conclusions can be drawn from the simulation. The multi-sliding surface control can restrain the overshoot and realize a quick track of the targeted value with high accuracy and strong robustness. In addition, the fuel consumption and CO2 emission of this sliding mode variable structure control is reduced by 4.6% compared with traditional PID control.
In this paper, active vibration control of carbon nanotube reinforced composite beams subjected to a temperature rise is studied. For this purpose, piezoelectric patches are used as sensors to measure the displacement of the beam and as actuators to implement control forces. The governing equation of motion of this beam is derived from the Euler–Bernoulli theory and Hamilton’s principle. Galerkin’s method is utilized to obtain the temporal ordinary differential equations. An optimal observer-based output feedback controller is designed by the linear quadratic regulator (LQR) methodology to ensure closed-loop stability. Simulation studies demonstrate the effectiveness of the proposed method.
This paper considers the problem of output tracking of a time-varying reference signal for a class of uncertain systems in the presence of actuator saturation. To achieve this capability, a new controller is proposed by robustifying the generalized composite nonlinear feedback control method with the integral sliding mode controller. Since the proposed controller may be saturated, a precise analysis is done to show its robust performance despite the presence of actuator saturation and model uncertainties. For this purpose, a theorem is given and proved that guarantees the robust output tracking via the proposed control law for three different cases of the saturation function and it is shown that even if the control signal is saturated, the proposed controller achieves output tracking of the time-varying reference signal. Also, in order to show the applicability of the proposed controller, it is applied on two practical systems, the XY-table and inertia wheel inverted pendulum. Computer simulations verify the theoretical results and also display the effective performance of the proposed controller.
The electro-hydraulic control loading system (EHCLS) of a flight simulator is mainly used to simulate the force feel of flying a real airplane. A double-loop model, including control and hydraulic mechanism of the EHCLS, is established and its force–displacement impedance is analysed to evaluate force tracking performances and its stability. An approximated feed-forward inverse model controller is designed using a zero phase error compensation method for expanding the frequency bandwidth of the inner loop because the identified force closed-loop model is a nonminimum phase system and its direct inverse model is unstable. Modelling error between the designed feed-forward inverse model and the actual plant is discussed and a damping compensator is presented to increase stability of the EHCLS. Theoretical analysis, simulation and experimental results show that the control methods presented are feasible and can effectively improve force feel fidelity and stability of the EHCLS.
In this paper, a wavelet-based low-pass filter (WBLPF) is proposed to improve the direct current (DC) component separation speed. To this end, three different methods are studied to implement discrete wavelet transform, and then they are compared with each other in term of transient response time, accuracy and computational cost. Afterwards, the conventional low-pass filter is replaced by the new WBLPF in the p-q method. This replacement increases the speed of DC component separation, and consequently the performance of the shunt active power filter is improved. To verify and examine the proposed method, a power system is simulated in MATLAB software and a prototype is implemented in the laboratory. The simulation and experimental results confirm the superiority of the proposed method.
This paper studies the stability analysis of the decentralized event-triggered H control with communication delays using the quadratic convex approach. Unlike the decentralized event-triggered mechanism (ETM), which only uses the information from the sensor itself by considering the communication topology of the wireless sensor network, a more general decentralized ETM is first proposed by using the information from both the sensor itself and its neighbours. Then, a time-delay system model with parameters of the decentralized ETM, directed graph information, communication delays and external disturbances is presented. In addition, novel delay-dependent asymptotic stability criteria are derived by using the augmented Lyapunov–Krasovski functional (LKF), which contains the cross terms of variables and quadratic terms multiplied by a higher degree scalar function. Unlike some prior results using the first-order convex combination property, our derivation applies the quadratic convex approach with the augmented LKF, which results in less conservatism. Moreover, sufficient conditions for the co-design of the controller and the decentralized ETM are obtained. Finally, numerical examples confirm the effectiveness of the proposed method.
In this paper, the observer-based consensus problem for nonlinear multi-agent systems is considered. The dynamics of each agent is given in general form of Lipschitz nonlinear system, and the communication topology among the agents is assumed to be undirected and connected. The leader-following case and leaderless case are discussed. In the former, it is assumed that the leader’s input is possibly nonzero and time-varying and only a subset of the following agents can access the state information of the leader. To track the active leader, a distributed adaptive consensus protocol, based on the relative-output information with its neighbouring agents, is proposed for each following agent. It is shown that under suitable conditions, all the following agents can track the leader under the designed adaptive controllers and observers. Following that, the leaderless case is probed. Finally, a numerical example is given to illustrate our obtained result.
The fault diagnosis of generator units is critical to guarantee the high efficiency of the electric system. However, detailed fault samples are difficult to obtain, and the distribution of fault samples usually shows the characteristics of unevenness and unbalance, which may lead to low fault diagnosis precision. Nevertheless, it has been seldom considered in the traditional classifier of fault diagnosis for generator units until now. In this paper, a novel fault classifier of weighted support vector data description (SVDD) with fuzzy adaptive threshold decision is proposed and applied in the fault diagnosis of generator units. To tackle the drawback that SVDD is sensitive to the distribution of samples, a novel SVDD model based on a complex weight is proposed. The complex weight is assigned with local density and size-based weight, while local density of each data point is obtained with the k-nearest neighbour approach and the size-based weight of each data point is computed according to the proportion of classes. Then the conventional SVDD is reformulated with the complex weights. Furthermore, new decision rules based on the relative distance and fuzzy adaptive threshold decision are applied to identify the class of testing samples. Finally, the proposed method is applied in the identification of several standard datasets, as well as the fault diagnosis for a turbo-generator unit. Experimental results and the engineering application reveal that the proposed method shows good performance in accuracy and universality, and is suitable for the fault diagnosis of generator units.
This paper investigates the adaptive feedback passification and disturbance attenuation problems for a class of switched nonlinearly parameterized systems. First, a state-dependent switching law and a set of adaptive feedback controllers with new control inputs are designed to render the resulting closed-loop system passive for a class of switched nonlinearly parameterized systems without external disturbance. Then, the new control inputs are designed to solve the disturbance attenuation problem. Second, a set of adaptive feedback controllers and a composite state-dependent switching law are designed to solve the adaptive feedback passivity-based disturbance attenuation problem for a class of cascaded switched nonlinearly parameterized systems. A numerical example shows the effectiveness of the proposed method.
An observer-based dynamic output feedback H controller is proposed for a class of two-dimensional (2D) uncertain discrete systems described by the Roesser model with actuator saturation, time-varying state delay and external disturbances. First, a delay-dependent Lyapunov stability condition is derived in linear matrix inequality (LMI) form which uses the reciprocal convex approach and H disturbance attenuation performance is also analysed. Secondly, a convex hull is adopted to represent the saturation nonlinearity. The H control synthesis for uncertain 2D discrete systems is described by a Roesser model subjected to actuator saturation and external disturbances using an observer-based dynamic output feedback approach. Some practical examples are provided to highlight the usefulness of the presented results.
In this paper, we investigate the problem of using sampled-data feedback to synchronize a slave (driven) system with a master (driver) system. Based on the domination approach, both state-feedback and output-feedback control methods using sampled-data are proposed to make the tracking error converge to zero. The problem is of practical importance since in practice the system state is transmitted as sampled signal, and very often only the output is measurable. The effectiveness of the proposed approach is illustrated by the simulation for a chaotic Chua oscillator.
This paper is concerned with the optimal boundary control of a non-dimensional non-linear parabolic system consisting of the Kuramoto–Sivashinsky–Korteweg–de Vries equation and a heat equation. By the Dubovitskii and Milyutin functional analytical approach, first in the fixed final horizon case we prove the Pontryagin maximum principle of the optimal control problem of this coupled system. Then under weaker additional conditions, we study the controlled system in the free final horizon case and present further investigational results of current interests. The necessary optimality conditions are established for optimal control problems in these two cases. Finally, a remark on how to utilize the obtained results is also made for illustration.
Initial alignment for a strap-down inertial navigation system (SINS) plays an important role in the following navigation and positioning operation. Initial alignment incorporates two stages: coarse and fine. This paper mainly investigates fine alignment for SINS under static base. A new fast SINS initial alignment scheme, a disturbance observer-based Kalman filter (DOBKF), is proposed to estimate the misalignment angles. As the name implies, the DOBKF is composed of a Kalman filter and a disturbance observer (DO). The Kalman filter is used to estimate horizontal misalignment angles, and the DO is applied to estimate the azimuth misalignment angle. In addition, when the estimations from the Kalman filter reach a steady state, they will be used as input for designing the DO. Compared with traditional filters, such as a Kalman filter used in initial alignment, the filter proposed by this paper not only greatly hastens the overall initial alignment process, but has comparable accuracy. Comparing simulation results shows that the proposed filter satisfies the requirement of SINS alignment.
The fabrication is described of a vibration sensor based on the structure of the sensitive hair of some insects. The biomimetic hair has a metal core wrapped with a polyvinylidene difluoride (PVDF) layer. Two surface electrodes were coated on the PVDF surface of the fibre. A single SMPF (symmetric electrodes of metal core piezoelectric fibre) pasted on a matrix can be used as a vibration sensor. We propose a theoretical model describing the cantilever beam structure of the vibration sensor. The SMPF can detect both vibration amplitude and direction of the matrix. It can also detect the matrix harmonic excitation frequency and amplitude. We prepared PVDF fibres with metal cores (diameter: 230 µm) with the mould drawing method and used the surface electrodes and the metal core to polarize the PVDF layer. When the SMPF is used as a sensor, we only use the output signal of the two surface electrodes. Our experiments indicate that the SMPF can detect both vibration amplitude and direction of the matrix as well as the harmonic excitation frequency and the vibration direction. Our experimental results are consistent with the theoretical considerations.
In this article, a novel guidance law derivation and new synchronization strategy are proposed for a virtual structure-based formation flight. These are designed using both aerodynamic and dynamics equations of aerial robots to facilitate implementation of the guidance and control laws. The guidance commands are derived in the form of acceleration based on a new analytical approach. These acceleration commands are converted to suitable inputs for the control system in the form of velocity, roll and pitch angles by employing an innovative strategy. In addition, a new synchronization strategy for virtual structure formation control is proposed. In this strategy, each agent utilizes the other agents’ actual position rather than their position errors. The proposed strategy is capable of shape formation flight using a passive sensor, such as vision sensors, for position detection of neighbour agents. This ability makes the proposed strategy more reliable than conventional synchronization methods. The mentioned strategy is further improved by self-tuning of the synchronization gain based on a fuzzy inference. The simulation of formation flight for a group of three fixed-wing aerial robots using six degrees of freedom models for each one reveals the merits of the proposed strategy. In fact, this approach significantly decreases the number of oscillations and corresponding amplitudes of position/orientation error for each agent. This is a crucial aspect of the mission performance for formation flight control.
This paper deals with internal model controller design via
In this paper, we propose a novel cooperative control scheme that solves the position synchronization problem of multiple robot manipulators, under parametric uncertainties, in the case when only position measurements are available. The synchronization controller, adaptation law and the filter that generates a velocity-related signal from the position tracking error are designed via a Lyapunov-based stability analysis. It is shown that the proposed method guarantees semi-global asymptotic convergence of the position synchronization error. The simulation results on five robot manipulators ensure the feasibility of the filter/controller mechanism.
A new computationally simple and precise model approximation method is described for large-scale linear discrete-time systems. By least squares matching of a suitable number of time moment proportionals and Markov parameters about
In this paper, the leader–follower formation control problem of autonomous over-actuated electric vehicles on a highway is studied. As the autonomous over-actuated electric vehicles have the characteristics of non-linearities, external disturbances and strong coupling, a novel coordinated three-level control system is constructed to supervise the longitudinal and lateral motions of autonomous electric vehicles. Firstly, an adaptive terminal sliding high-level control algorithm is designed to compute a vector of total forces and torque of vehicles, and the stability of the high-level control system is proven via Lyapunov analysis where uniform ultimate boundedness of the closed-loop signals is guaranteed. Then, a pseudo-inverse control allocation algorithm, which can achieve fault tolerance and reconfiguration of the redundant tyre actuation system, is presented to generate the desired longitudinal and lateral tyre forces. Then, a separate low-level controller consisting of an inverse tyre model and two inner loops for each wheel is designed to achieve its desired forces. Finally, simulation results demonstrate that the proposed control system not only enhance the tracking performance, but also improve the stability and riding comfort of autonomous over-actuated electric vehicles in a platoon.
This paper proposes a theoretical and practical approach for determining the stabilizing gain intervals for two-input, two-output (TITO) systems with a constant diagonal controller. Our main contribution is to present an algorithm which is more practical than the stabilizing methods that depend on the Nyquist stability criterion. Numerical examples illustrate the effectiveness of the proposed algorithm for TITO systems with reducible or irreducible characteristic equations.
We deal with the state consensus problem of a general heterogeneous linear multi-agent system under a time-invariant and directed communication topology. First we adopt a general linear consensus protocol consisting of two parts. One is a state feedback of the agent for independently regulating its dynamics, and the other is a cooperative term in a generalized feedback form of the relative states between the agents. Then we propose a state-linear-transformation to equivalently transform the state consensus problem into a partial stability problem. Therefore, the result from the partial stability theory is applied to derive a sufficient and necessary algebraic criterion of consensus convergence, which is expressed in terms of the Hurwitz stability of a real matrix constructed from the parameters of both the agents’ models and the protocol. Meanwhile, an analytical formula of the consensus function is presented. Based on the criterion, we propose a design procedure of the gain matrices in the protocol by solving a bilinear matrix inequality.
This paper presents an adaptive nonsingular terminal sliding mode controller for a bearingless permanent magnet synchronous motor. In order to rapidly converge state variables associated with terminal sliding mode control, an adaptive variable-rated exponential reaching law, in which the L1 norm of state variables is introduced, is proposed for the second-order uncertain nonlinear dynamical system. Exponential and constant reaching speed can adaptively adjust according to the distance between state variable and equilibrium point, which can shorten the reaching time and weaken system chattering. The mathematical models for rotating speed and the rotor radial displacement of the bearingless permanent magnet synchronous motor system are set up. The proposed method is then applied to the speed and radial displacement control. Simulation results are provided to validate the effectiveness of the proposed method.
This paper studies the modelling and stabilization problem for a class of networked control systems (NCSs) with random time delays and packed dropouts based on the average dwell time (ADT) switching approach. A new effective sampling method is presented to deal with time-varying delays, which finally reduces the high dimension and complexity of the NCS model. A class of subsystems is developed for NCS based on a set of switching signals triggered by discretized time delays and packet dropouts. By constructing multiple quadratic Lyapunov-like functions that allowed both decreasing and increasing, sufficient switched stability conditions are derived and a set of mode-dependent feedback controllers are designed for each active mode based on the ADT switching control method to guarantee the stability of the proposed switched NCS with admissible ADT. A do-loop linear matrix inequalities (LMI) optimization problem is formulated to find the control law without predefining the switching sequence and the times when network-induced factors occur. A numerical example is given to illustrate the effectiveness of the proposed method.
In the combustion system of a boiler, oxygen content in the flue gas is a significant economic parameter for combustion efficiency. As a combustion system is highly complex and there are many constraints in a real process, traditional control cannot achieve satisfying performance in the practical oxygen content tracking control problem. In this paper, we build a combustion process model with a data-driven method and present a multiple-model-based fuzzy predictive control algorithm for the oxygen content tracking control. The combustion process model is presented as a multiple-model form, which can represent the real process more accurately. A data-driven method with fuzzy c-means clustering and subspace identification is used to identify the model parameters. Then, model predictive control integrated with a fuzzy multiple-model is used to control the oxygen content tracking problem. As the coal manipulated variable is decided by the load demand in the real process, a real-time measured value is applied to the process. All data used to obtain the process model is historical real-time data generated from a 300-MW power plant in Gui Zhou Province, China. Real-time simulation results on the 300-MW power plant show the effectiveness of the modelling and control algorithms proposed in this paper.
This paper addresses the robust stabilization problem of first-order uncertain systems. To treat the robust stabilization problem, an interval-based stabilization method using stability conditions of the non-commensurate elementary fractional transfer function of the second kind is developed. Some analytic expressions are determined to compute the set of all stabilizing controller parameters and plot the stability boundary. A robust performance control is also developed to fulfil some desired time-domain performances as the iso-overshoot property. The fractional controller can be used combined with the Smith predictor to control a first-order system with time delay and achieve desired specifications. Numerical examples are presented to illustrate the obtained results.
Large-scale ultrasonic positioning systems have recently shown substantial improvement, according to the current great interest concerning large-scale metrology applications in many different fields of manufacturing industry. However, the sound velocity is greatly influenced by the ambient temperature especially in large-scale applications. The traditional sound velocity compensation method mainly uses the average temperature in measurement space based on an assumption that the temperature field is uniform and stable. As the assumption is invalid in most cases, especially in industrial measurement environments, the traditional compensation method will bring obvious errors. To reduce these errors caused by sound velocity, this article proposes a novel compensation method by constructing a three-dimensional temperature field of measurement space through heat transfer theory as well as the finite element method, and then estimating the distance accurately using a couple iterating algorithm. Verification experiments demonstrate that the ranging deviation estimated through the proposed method may keep within 0.5 mm in 5.4x2 m measurement space, whereas the average distance measurement uncertainty is about 0.25 mm.
Modified backstepping control is proposed for an under-actuated rotary double inverted pendulum. The system has actuated rotary base joint with which two unactuated links are attached. The proposed control design is a three step process for de-coupled system model. In the first stage, a backstepping controller is designed for each of the active and passive joints. In the second stage, compensation is introduced in the respective control efforts to cater for uncertain terms based on Lyapunov function for each joint. Finally, the controllers obtained in the two stages are combined to form a total control law. The performance of the proposed control scheme is evaluated by convergence analysis and simulations.
In this study, a fault tolerant heading control system is designed for a one-third scale fixed wing vertical takeoff-and-landing unmanned aerial vehicle, Turac. A nonlinear six degrees-of-freedom (DoF) mathematical model is obtained and linearized at the calculated trim flight condition. A proportional heading control system is designed as a nominal horizontal flight controller. Detection and isolation of the faults that can occur during flight are performed by Kalman filters which are designed individually for each sensor output. After the fault isolation process the obtained fault data is fed to the reconfigurable Kalman filter. Then the feedback signal from the faulty sensor is blocked and the estimated output from the reconfigurable Kalman filter is fed to the control system. So, the closed-loop system could follow the reference signal without updating the controller’s parameters. Simulation studies are performed on the closed-loop system for faulty sensor situations.
Dynamic resource allocation in a cloud environment has become possible using virtualization technologies in cloud computing. One of the applications of these technologies is offering various applications by Software-as-a-Service (SaaS) infrastructures. Unfortunately, due to request rate increments in cloud rush hours, the related server cannot serve all the requests according to the service level agreement. Hence, the cloud provider’s quality of service will decrease. Thus a mechanism is required to control the admission rate of requests for cloud servers. In this study, an intelligent controller is designed and implemented on a field-programmable gate array (FPGA) in order to control the admission rate of requests for a SaaS server in the cloud. The controller is based on a brain emotional learning-based intelligent controller (BELBIC). First, an analytical model of a server is proposed and simulated, which shows the behavioural characteristics of a real server. Next, the BELBIC is designed to control the admission rate of the server. Finally, the system is implemented on FPGA hardware and simulated by a synthetic cloud workload in a hardware-in-the-loop manner. In order to compare the performance of the BELBIC, an adaptive neuro-fuzzy inference system (ANFIS) controller in addition to the popular PID controller is provided. The controllers’ efficiencies are compared in terms of server utilization, admission rate, drop rate of requests and the agility of the controllers. The results proved that the BELBIC offers faster rise time compared with the PID controller, which leads to better cloud utilization and a smaller number of dropped requests.
A new solution is presented to the problem of controlling the motion of a crane’s suspended load through arbitrarily complex, 3D paths through the crane’s manoeuvre space. A generalized boom crane arrangement is considered, so that gantry and luffing arrangements are included as particular cases. Thus the crane’s boom slews about a central, vertical (tower) axis. This boom is either a horizontal gantry with a trolley moving radially along it, from which the load can be winched, or a jib, which can rotate in the vertical plane, with the hoisting cable passing over a pulley attached at its end point. In either case, there are three directly controlled motion variables, the effects of which on the suspended payload’s motion are strongly cross-coupled. The challenge is to enable the payload to follow the desired 3D path as closely as possible during the manoeuvre, and come to rest rapidly at target, by directly controlling these three actuating motions. Thus the controller must achieve position control combined with active swing suppression throughout the manoeuvre and on arrival at the desired end point. A model is developed of the generalized crane for both gantry and luffing crane types. The proposed control strategy is then applied and tested on this model. The controller is based on mechanical wave concepts. When applied to the model, it is shown to be very effective. It is accurate, robust to system changes and actuator limitations, very stable, requires sensing only at the trolley (and not at payload), and is easy to implement.
In this paper, adaptive compensation with a robust integral of the sign of the extended error (RISEE) feedback is developed for high precise tracking control of systems in the presence of simultaneous structured and unstructured uncertainties. To handle various uncertainties existing in the system in one controller, we propose an adaptive RISEE controller (ARISEE), in which an adaptive law based on the improved discontinuous projection method is synthesized to handle parametric uncertainties and the RISEE robust term to attenuate unmodelled disturbances. Moreover, the present controller does not need a priori knowledge on the bounds of the lumped disturbances and the gain of the designed robust control law can itself be tuned. The major feature of the proposed controller is that it can theoretically guarantee global asymptotic tracking performance with a continuous control input, in the presence of parametric uncertainties and unmodelled disturbances via Lyapunov analysis. Comparative numerical results verify the effectiveness of the proposed non-linear controller.
This paper is concerned with the problem of the master–slave synchronization of chaotic Lur’e systems with multiple time delays in their states and transmission line. Based on the Lyapunov–Krasovskii functional, some delay-dependent synchronization criteria are obtained and formulated in the form of linear matrix inequalities (LMIs) to ascertain the global asymptotic stability of the error system such that the slave system is synchronized with the master. With the help of the LMI solvers, the time-delay feedback control law can easily be obtained. The effectiveness of the proposed method is illustrated using some numerical simulations performed on two chaotic systems.
This paper concerns the filtering problem for a class of continuous-time Markovian jump linear systems, where the Markovian jump is supposed to frequently occur in some short time intervals. For this class of Markovian jump system, the boundedness of estimation error deserves our investigation. By introducing the concepts of stochastic boundedness with respect to a finite-time interval, an observer ensuring the estimation error bounded in a prescribed boundary is constructed and the result is extended to the
This paper develops a systematic iterative learning control (ILC) strategy for systems with mismatched disturbances. The systems with mismatched disturbances are more general and widely exist in practical engineering, where the standard disturbance observer based ILC method is no longer available. To this end, this note proposes a novel ILC scheme based on the disturbance observer, which consists of two parts: a baseline ILC term for stabilizing the nominal system and a disturbance compensation term for attenuating mismatched disturbances by choosing an appropriate compensation gain. It is proven that the performance of the closed-loop system is effectively improved. Finally, the simulation analysis for a permanent-magnet synchronous motor servo system demonstrates the feasibility and efficacy of the proposed method.
Rotor speed control of wind turbines is a key factor in achieving the maximum power of wind. It is known that a high-performance controller can significantly increase the amount of energy that can be captured from this source. The main problem regarding this issue is the lack of information about the correct dynamic model of the system. This uncertainty of the model is generally associated with unknown parameters (structured uncertainty) and/or external disturbances (unstructured uncertainty). Some adaptive and robust control approaches are developed in the literature in order to deal respectively with structured and unstructured uncertainties. In this paper, to compensate for both types of uncertainty, a robust controller, which includes an adaptive feedforward term, is proposed to track the optimal speed. In addition to considering the uncertainties, another advantage of the presented approach is that, using a smooth control effort, it provides global asymptotic tracking. A complete stability proof of the system is presented, and simulation results illustrate the effectiveness of the controller.
For the control problem of bridge cranes, it is challenging to realize fast transportation and efficient swing suppression simultaneously. Motivated by this observation, in this paper, we aim to propose a nonlinear controller achieving these objectives by constructing a desired Lyapunov function. In particular, a constructive Lyapunov function is introduced in a segmented manner. Based on that, a nonlinear control method rendering the dissipation inequality with respect to the constructed Lyapunov function is proposed straightforwardly, which achieves precise trolley positioning along with efficient payload swing elimination. The corresponding stability and convergence analysis is guaranteed by Lyapunov techniques and LaSalle’s invariance principle. Simulation and experimental results are provided to demonstrate the effectiveness and feasibility of the proposed method.
This paper aims to study stability for discrete-time non-linear singular systems with switching actuators. A sufficient condition is addressed to ensure that non-linear closed-loop singular systems are input-to-state stable via average dwell time approach and the iterative relationship of discrete-time systems. In the stability criterion, we neither construct a certain Lyapunov function, nor design the specific structure of the control inputs. It is much easier to design each sub-controller of switching actuators via the proposed condition. Finally, a numerical example is provided to demonstrate the feasibility and effectiveness of the results obtained.
An autonomous humanoid robot (HR) with learning and control algorithms is able to balance itself during sitting down, standing up, walking and running operations, as humans do. In this study, reinforcement learning (RL) with a complete symbolic inverse kinematic (IK) solution is developed to balance the full lower body of a three-dimensional (3D) NAO HR which has 12 degrees of freedom. The IK solution converts the lower body trajectories, which are learned by RL, into reference positions for the joints of the NAO robot. This reduces the dimensionality of the learning and control problems since the IK integrated with the RL eliminates the need to use whole HR states. The IK solution in 3D space takes into account not only the legs but also the full lower body; hence, it is possible to incorporate the effect of the foot and hip lengths on the IK solution. The accuracy and capability of following real joint states are evaluated in the simulation environment. MapleSim is used to model the full lower body, and the developed RL is combined with this model by utilizing Modelica and Maple software properties. The results of the simulation show that the value function is maximized, temporal difference error is reduced to zero, the lower body is stabilized at the upright, and the convergence speed of the RL is improved with use of the symbolic IK solution.
In order to improve the convergence of a sliding variable in super-twisting algorithm (STA), two linear correction terms are added to the classical STA to form a structure of double closed-loop feedback. The new regulation mechanism can accelerate the sliding variable approaching the sliding surface and simultaneously limit knotty overshoot. A series of simulation results indicate the new modified STA can shorten the settling time of the sliding variable effectively and provide stronger robustness. Moreover, the position tracking test results on DC servo system validate the advantageous performance of the modified STA proposed in this paper.
A new algorithm is presented for learning the Takagi–Sugeno (T-S) fuzzy model from data by improved Free Search algorithm (IFS), where the rule structure (selection of rules and number of rules), input structure (selection of inputs and number of inputs) and parameters of the T-S fuzzy model are all represented as individuals of the IFS and evolved together such that the optimization of the rule structure, the input structure and the parameters can be achieved simultaneously. The developed IFS-T-S model is used for the prediction of melt index in an industrial propylene polymerization process and the results show that the proposed IFS-T-S model has a good fitting and prediction ability.
In this paper, a novel adaptive global sliding mode control technique is suggested for the tracking control of uncertain and non-linear time-varying systems. The proposed scheme composed of a global sliding mode control structure to eliminate reaching mode and an adaptive tracker to construct the auxiliary control term for eliminating the impacts of unwanted perturbations. Using the Lyapunov direct method, the tracking control of the non-linear system is guaranteed. Moreover, superior position tracking performance is obtained, the control effort is considerably decreased and the chattering phenomenon is removed. Furthermore, using adaptation laws, information about the upper bounds of the system perturbations is not required. To indicate the effectiveness of the suggested scheme, three simulation examples are presented. Simulation results demonstrate the superiority and capability of the offered control law to improve the transient performance of a closed-loop system using online adaptive parameters.
This research develops a multiple-surface sliding mode control (MSSC) approach for position control of a servo-pneumatic system in the presence of mismatched uncertainties due to the friction force of the cylinder sealing. Servo-pneumatic actuators have many applications such as industrial automation, haptic interfaces, rehabilitation robots and non-invasive surgeries. Non-linearities due to internal and external disturbances, such as the friction force between the piston seal and cylinder wall, make it difficult to achieve adequate performance from these actuators. Thus, modelling and identification of friction parameters is an essential part of the controller design procedure. A simple model for friction such as the Stribeck model may be used in order to reduce the complexity of the identification procedure. In addition, a bounded uncertainty owing to the unmodelled dynamics of friction is considered. The lack of direct measurement for systems velocity necessitates finding a solution to estimate this parameter. One answer to this problem is to employ a high-gain observer. Whereas mismatched uncertainties appear in the state space equation of the system before control input, control input cannot apply directly to them. In the proposed framework, an MSSC scheme has been used to cope with these types of uncertainties. Asymptotic stability of the closed-loop system is proven by using the Lyapunov method; experimental results show that the proposed controller can deliver a good tracking performance and is robust to uncertainties. Experimental results validate the controller performance.
This paper presents a scheme for satellite multi-sensor fault-tolerant attitude estimation. It can both detect a faulty sensor online and conduct fault tolerance in time. First, a satellite attitude estimator based on error states is presented as the unified filter algorithm, in which the filter state equation is built with the gyro model and satellite kinematics, and the measurement equations are built with angle sensors. Meanwhile, typical fault models of the angle sensors are given. Then, a fault-tolerant federated Kalman filter (FTFKF) scheme is designed. Its three sub-filters include the respective angle sensors and share the public gyro measurement. Under the sensor fault condition, a FTFKF can detect the faulty sensor by contrasting and analysing the dimensionless fault detection factors and then selectively fusing the sub-filters’ outputs to enable the satellite attitude determination accuracy to approach normal. The comparative analysis of simulation results in typical fault cases shows that the proposed satellite multi-sensor fault-tolerant attitude estimation scheme may achieve the expected fault-tolerant performance. It is promising for enhancing the reliability of satellite attitude determination and control systems.
To improve the performance of three-phase voltage source pulse-width modulated (PWM) rectifiers (VSR) under unbalanced grid voltage conditions, a fixed-frequency current predictive control (CPC) strategy is presented. Instantaneous power of the three-phase VSR is analysed in a two-phase stationary frame. The calculation method for the reference current is improved to achieve the power stability at the AC side of the rectifier. Based on the current predictive model, the optimal duration of the voltage vectors is computed under the restricted condition of minimizing current error at α- and β-axes in fixed intervals. The control system is free of synchronous rotation coordinate transformation, and avoids positive and negative sequence decomposition, which simplifies the calculation. The simulation and experimental results show that the proposed control strategy is able to eliminate the AC current distortion effectively and depress DC link voltage fluctuation under unbalanced grid voltage. Furthermore, the control strategy has faster dynamic response ability, enhancing the control performance of the three-phase VSR system.
Adaptive local iterative filtering (ALIF) is a new signal decomposition method that uses the iterative filters strategy together with an adaptive and data-driven filter length selection to achieve the decomposition. The complexity of wind power generation systems means that the randomness and kinetic mutation behaviour of their vibration signals are demonstrated at different scales. Thus it is necessary to analyse the vibration signal across multiple scales. A method based on ALIF and singular value decomposition (SVD) was used for the fault diagnosis of a wind turbine roller bearing. The ALIF method is used to decompose the bearing vibration signal into several stable components. The components, which contain major fault information, are selected to build an initial feature vector matrix. The singular value of the matrix is computed as the feature vectors of each bearing fault. The feature vectors embody the characteristics of the vibration signal. The nearest neighbour algorithm is used as a classifier to identify faults in a roller bearing. Experimental data show that the proposed method can be used to identify roller bearing faults of a wind turbine.
Accurate prediction of the remaining useful life of lithium-ion batteries plays a significant role in various devices and many researchers have focused on lithium-ion battery reliability and prognosis. A particle filter (PF) is an effective filter for estimation and prediction of time series data where model structure is available. The prediction accuracy of a PF depends on two key factors: parameter initialization and the state equation. In this paper, parameters are estimated using a PF and two empirical exponential models, i.e. the exponential model and improved exponential model, are used to track the battery capacity degradation; each model uses a different state equation. Experiments were performed to compare prediction accuracy using the related parameters estimation model with that using the capacity decline model; this paper compares the effects of the different state equations on the lithium-ion battery remaining useful life prediction. The experimental results show the merits of the capacity decline model based on particle filtering. The capacity decline model PF is more suitable for estimating the battery capacity trend in the long term.
This paper presents an output-based command shaping (OCS) technique for an effective payload sway control of a 3D crane with hoisting. A crane is a challenging and time-varying system, as the cable length changes during the operation. The OCS technique is designed based on output signals of an actual system and reference model, does not require the natural frequency and damping ratio of the system, and thus can be utilized to minimize the hoisting effects on the payload sway. The shaper was designed by using the derived non-linear model of a 3D crane. To test the effectiveness of the controller, simulations using a non-linear 3D crane model and experiments on a lab-scale 3D crane were performed and compared with a zero vibration derivative (ZVD) shaper and a ZVD shaper designed using an average travel length (ATL) technique. In both the simulations and the experiments, the OCS technique was shown to be superior in reducing the payload sway with reductions of more than 56% and 33% in both of the transient and residual sways that were achieved when compared with both the ZVD and the ATL shapers, respectively. In addition, the OCS technique provided the fastest time response during the hoisting. It is envisaged that the method can be very useful in reducing the complexity of closed-loop controllers for both tracking and sway control.
One of the controllers used in load–frequency control systems is the PI controller, taking account of time delay originating from measurement and communication. In control systems, along with the use of the fractional-order controller, computing parameter space exhibited stable behaviour on the controller parameters and analysing its efficiency have become a significant issue. This study focuses on computing the effects of the fractional integral order (α) on the stable parameter space for the control of a one-area delayed load–frequency control system in the case of a fractional-order PI controller. The effect of time delay on the stable parameter space is also investigated at different fractional integral orders (α) in the time-delayed system with fractional-order PI controller. For this purpose, a characteristic equation of the delayed system with the fractional-order PI controller is obtained, and the stable parameter spaces of the controller are computed according to the fractional integral order (α) and time delay () values using the stability boundary locus method, which is graphics based. Moreover, the generalized modified Mikhailov criterion is used for testing the stability region on the Kp–Ki plane. The obtained results verified that the stability region on the Kp–Ki plane change depending on the α and .
In this paper, we consider the problem of controlling a team of Quadrotors that cooperatively grasp and transport a common payload in three dimensions in the presence of external disturbances and parametric uncertainties such as wind field effects. The main contribution of this work is to propose a cooperative control algorithm based on a decentralized strategy. This algorithm consists of two main parts: first calculating the control vectors for each Quadrotor using Moore–Penrose theory and second combining these control vectors with individual control vectors, which are obtained from a closed-loop non-linear robust optimal controller. In this regard, a robust optimal sliding mode controller (ROSMC), which incorporates the state-dependent Riccati equation (SDRE) method with sliding mode control (SMC) technique, is designed. It also has the capability of maximum dynamic load carrying capacity (DLCC) to increase the carrying capacity and the efficiency of the group of Quadrotors. The proposed method inherits the advantages of both approaches including robustness against model uncertainties and high flexibility in designing the control parameters to provide an optimal solution for the non-linear dynamic of the system. The control algorithm is based on the Lyapunov technique, which is able to provide the stability of the end-effecter during tracking of the desired trajectory with acceptable precision. Finally, the simulation results demonstrate the effectiveness of the control strategy for the cooperative Quadrotors to grasp and transport a common payload in various manoeuvres.
This paper presents the development of simple but powerful path-following and obstacle-avoidance control laws for an underactuated autonomous underwater vehicle (AUV). Potential function-based proportional derivative (PFPD) as well as a potential function-based augmented proportional derivative (PFAPD) control laws are developed to govern the motion of the AUV in an obstacle-rich environment. For obstacle avoidance, a mathematical potential function is used, which formulates the repulsive force between the AUV and the solid obstacles intersecting the desired path. Numerical simulations are carried out to study the efficacy of the proposed controllers and the results are observed. To reduce the values of the overshoots and steady-state errors identified due to the application of PFPD controller a PFAPD controller is designed that drives the AUV along the desired trajectory. From the simulation results, it is observed that the proposed controllers are able to drive the AUV to track the desired path, avoiding the obstacles in an obstacle-rich environment. The results are compared and it is observed that the PFAPD outperforms the PFPD to drive the AUV along the desired trajectory. It is also proved that it is not necessary to employ highly complicated controllers for solving obstacle-avoidance and path-following problems of underactuated AUVs. These problems can be solved with the application of PFAPD controllers.
The development of hybrid vehicular power systems has been conducted for decades to improve transportation quality mainly in terms of environment pollution and fuel economy. Hence, hybrid electric vehicular systems are considered an attractive and potential solution in the long run to replace conventional combustion engine vehicles. In this paper, a scaled-down vehicular powertrain test bench is designed and constructed utilizing a hybrid fuel cell/battery energy sources. The performance of the proposed test bench is also investigated experimentally to explore the modes of operation for system components under various road conditions. Load-following energy management strategy is implemented experimentally in this hybrid configuration. The concepts that can be learned from such test bench are certainly essential for any future implementation on real full-size vehicles. In this study, it is shown that even though fuel cells have a good energy-to-weight ratio, they have a slow response and that is why they must be combined with other fast-response energy sources like a battery or supercapacitor. The test bench is mainly built to explore the implementation of various energy management strategies and control algorithms without the need to have a real vehicle and an automotive test track. In addition, it is an excellent platform for training highly qualified automotive engineers and university undergraduate students as well as automotive researchers.
This paper proposes two novel methods for fault detection in non-linear processes. These methods apply a Gaussian process (GP) to model the underlying process, and then the extended Kalman filter (EKF) and square root cubature Kalman filter (SCKF) are used to detect faults. Accordingly, two approaches called the Gaussian process–extended Kalman filter (GP-EKF) and Gaussian process–square root cubature Kalman filter (GP-SCKF) are proposed. The most important characteristic of these proposed methods is that there is no need for an accurate model of the system. Therefore, these methods are considered non-parametric approaches of fault detection in non-linear systems. To illustrate the performance of these algorithms in fault detection, they have been used in a continuous stirred-tank reactor system (CSTR). Both proposed methods are able to detect sensor faults at an early stage.
The present research attempts to address an automated optimization-based image-embedding approach through a levels-directions decomposition framework. The subject behind the present approach is to design colour image watermarking with a focus on contourlet representation, whereas the watermarking intensity is accurately calculated via an optimization algorithm with constraint. In the approach presented here, a number of performance monitors for watermarked and logo images are realized to deal with a new fitness function in the aforementioned optimization algorithm. It is worth noting that the first performance monitoring is organized based on the peak signal-to-noise ratio and the structural similarity, whereas the second one is organized based on the normal correlation and the bit error rate, respectively. There is a scrambling module to represent the logo information in disorder, where a number of attacks are simultaneously applied to the watermarked image in order to adjust the appropriate value for watermarking intensity to realize a robust and efficient solution. The ability of a decision maker system is manually taken into account for choosing the best levels and the corresponding directions regarding the contourlet representation, and the investigated results are considered in a number of well-known colour space models including RGB, YIQ and YCbCr. The key contribution of the present research is made based on the new integration of a set of subsystems employed in colour space models under the embedding and de-embedding processes in the contourlet representation, and the watermarking intensity is acquired through the optimization algorithm with constraint to present the competitive outcomes with respect to state-of-the-art benchmarks. The procedure for extracting the information concerning the logo image from the processed watermarked image under a number of attacks is implemented through the approach proposed, whereas there is no information about the original image and the watermarking intensity to be processed.
The aim of the study is to develop a measuring and presetting system for tool electrodes used for machining rimmed turbine blisks. A data-sampling method based on voltage monitoring was established as the foundation of this study. The proposed system, which consisted of three parts (mechanical, electric control and computer numerical control system software), was then realized. The method for extracting characteristic points from the theoretical model of the tool electrode and the bicubic B-spline algorithms for reconstructing the actual model of the tool electrode were established. A simulation analysis of the surface deviation was conducted to verify the extracting method and the algorithms. Next, a suitable accuracy evaluation method was developed to evaluate the accuracy of the tool electrode. Several experiments were performed to verify the performance of the system. Currently, the system is applied to practical production and has shown satisfactory results.
Generally, the machining vibration frequency spectrum is dominated by the tooth cutting frequency and its harmonics, the part structure and its natural frequency, and the spindle-tool subsystem natural frequency, exhibiting full-oscillatory behaviour. In order to identify the machining status, especially for those thin-walled workpiece machining, the on-machine detected monitoring signals with noise should be decomposed precisely. Actually, the signals’ inherent characteristics, such as the Q-factor, could be employed. In this article, decomposition of the full-oscillatory components from noisy machining vibration signals by minimizing the Q-factor variation is presented. The Q-factor will be calculated using quadratic interpolation of linear prediction coefficients. On this basis, the measured signals can be decomposed into high-, low- and residual-oscillatory signal components using the sparsity-enabled signal analysis. Furthermore, the signal decomposition process is repeated iteratively until the minimization of the Q-factor variation. Finally, the simulation and the thin-walled machining experiments were designed. From comparison of the signal decomposition results with the wavelet packet transform (WPT), it was shown that the signal decomposition accuracy and reliability using the proposed strategy has been improved significantly.
This paper proposes a discrete-time sliding mode control (DT-SMC) algorithm for uncertain linear systems with time-varying delay on the state. The parameter uncertainties and the external disturbances are assumed to be unknown but norm-bounded. The sliding mode existence depends on that of the stable linear sliding surface, which is ensured by a sufficient condition that depends on the delay bounds and is derived using LMI. A numerical example as well as an application to the Two-Area Four-Machine power system has been presented to illustrate and confirm the usefulness and the effectiveness of the proposed control strategy.
This paper presents the impact of text and background colour combinations on the legibility of characters and readability of text presented on video display units (VDUs). The stimulus features influencing the operator’s response latency and accuracy are analysed in two experiments, examining the effects of text–background colour combinations and stimulus exposition on the ability of character and word identification on VDUs. This is analysed for the purpose of improving the information presentation in control information systems in Serbian underground coal mines. The legibility of characters based on the main colour combinations was tested by focusing on 30 participants. The first experiment required the participants to identify the legibility of uppercase alphabetic characters and numbers randomly selected and presented on a VDU. The visual performance was expressed by the number of correctly identified characters. The second experiment required the participants to identify the readability of the presented characters, based on the colour combinations of the characters and the background, in order to identify the best possible combination of colours for fast identification of information, which is very important in the detection of fast-changing unwanted events. The results of the experiments suggested that the character recognition was strongly influenced by character/background colour combination and exposition. The adequate selection of these variables can significantly improve the legibility of characters, and improve the speed and the accuracy of the identification of potential problems in underground coal mines during emergency situations.
A hydraulic excavator has a number of devices attached, the operating speeds of which are important performance indicators for performance evaluation and end-of-line inspection. This paper proposes an online and low-cost detection method to estimate the operating speed based on the peak detection of the main pump outlet pressure. A pressure sensor is installed on the main pump discharge point of a hydraulic excavator to collect real-time pressure data during operation. Then the wavelet method is deployed to denoise the obtained pressure data so that two special peak points are captured as start and end points for accurately estimating the operating time and speed of excavator devices. The proposed system is used for end-of-line inspection and performance evaluation of hydraulic excavators. Experimental results from 510 groups of data show that the proposed method can achieve a high success rate of 95.1%, and a high detection efficiency that is 60% faster than manual measurement.
This paper presents adaptive time-delay control (TDC) with a supervising switching technique (SST) for controlling robot manipulators. Two adaptive techniques are used to enhance the TDC. The control gain of TDC is adaptively tuned using a class of Nussbaum functions. With Nussbaum functions, compared with conventional TDC using a constant gain, the proposed control using Nussbaum functions can deal with inertia parameter variations due to the movement of a robot manipulator. The SST is used to deal with discontinuous disturbances. The proposed control is model-free, highly accurate, robust and adaptive.
A weak average consensus problem is investigated for a distributed multi-agent system with partial flowing nodes (MASPFN), which is composed of subsystem I and subsystem II: subsystem I consists of a fixed agent set, whereas subsystem II consists of a varying agent set in which some agents may join in or quit from the multi-agent system with partial flowing nodes randomly. The weak average consensus refers to the fact that states of all agents in subsystem I approach a common value under the influence of subsystem II. Based on the Lyapunov function and the iterative method, several consensus criteria are obtained. One numerical example shows the reliability of the proposed methods.
This paper addresses the distributed output regulation problem of switched multi-agent systems subject to input saturation. Two types of independent switching signals are considered in this work. Each agent is described by a switched linear system with an average dwell time and input saturation. The interconnection topologies are also switched subject to dwell time. Since not every agent can obtain the information of the exosystem, the distributed feedback controller is designed. An agent-dependent average dwell time method with an adjustable parameter based on algebraic Riccati equations is proposed to solve the output regulation problem. Finally, two examples are provided to illustrate the electiveness of our results.
The robustly resilient memory control problem is addressed for a class of switched systems with time delay under asynchronous switching. By resorting to the piecewise Lyapunov–Krasovskii functional, an asynchronous memory controller is designed to ensure the exponential stability of the closed-loop system. Here, the piecewise Lyapunov–Krasovskii functional means that for the activated subsystem, the associated Lyapunov–Krasovskii functional on the unmatched interval is different from that on the matched interval. Then an asynchronously resilient memory controller is derived to guarantee that the corresponding closed-loop system is robustly exponentially stable for all the admissible uncertainties. All the conditions are cast into the form of linear matrix inequalities. Finally, a numerical example is provided to illustrate the validity of the proposed results.
A fuzzy proportional-integral-derivative (PID) controller is designed using time-delay estimation (TDE). In this paper, we show that, in a discrete-time domain, the fuzzy PID controller is a superset of a time-delay controller (TDC). In other words, with certain fuzzy parameter conditions, the discrete fuzzy PID controller is equivalent to the discrete TDC. Tuning the fuzzy PID controller can be started by exploiting the TDC: a simple, efficient and effective controller. The initial fuzzy parameters can be obtained with equivalence relationships between the fuzzy PID controller and the TDC. Furthermore, the performance of the fuzzy PID controller can be enhanced by using the non-linear control surface described by various fuzzy parameters. To this end, a fuzzy TDC is proposed using the TDE and the desired fuzzy error dynamics, and a conversion formula from the fuzzy TDC to the fuzzy PID controller is derived in a discrete-time domain. The effectiveness of the proposed method is verified through experiments on a PUMA-type robot manipulator.
The electro-hydraulic shaking table is investigated, in the present paper, to simulate the vibrational working environment of industrial components and equipment. Adaptive robust control can be applied to the shaking table system because electro-hydraulic systems suffer from internal parameter uncertainties and external disturbances. However, the adaptive robust controller design is complicated and has a large computational cost owing to the ‘explosion of terms’ problem. Thus dynamic surface control is applied in the design procedure of adaptive robust controllers to overcome the ‘explosion of terms’ problem. In this work, dynamic surface adaptive robust control is proposed. It simplifies the designed procedure of the controller and decreases its computational cost. Firstly, the structure of a shaking table is formulated and the operation principles of the shaking table, including the hydraulic and control principles, are analysed. A change is made in the mechanical-hydraulic system of the fluid circuit to address the problem of changing the vibration direction. Secondly, a dynamic model of a shaking table is proposed. Based on analysis of this model, the design of a dynamic surface adaptive robust controller for a shaking table is presented so as to improve its performance. Finally, comparative simulations and experiments are carried out. The comparison of performance results with proportional-integral-derivative control verify the correctness of the hydraulic scheme and control principle, as well as the high-performance of the dynamic surface adaptive robust controller. The shaking table achieves a guaranteed dynamical performance and tracking accuracy for the output in the presence of parameter and load uncertainties.
This article is concerned with the prediction of time delays and a control method for networked control systems (NCSs). We propose to predict the network-induced delays online with least squares support vector machines (LSSVM) optimized by particle swarm optimization (PSO). Then self-tuning proportional–integral–derivative (PID) control with variable delays is adopted in the NCSs. The recursive least squares (RLS) method is used to identify the parameters of the controlled objects, which can adapt to changes in dynamic models. Simulations verified the excellent performance of network delay prediction and networked control, and stable closed-loop control can be realized.
In this study, the given-time H consensus problem over networks with directed information flow and Markov jump topologies is addressed. Our focus is on keeping the disagreement dynamics of networks confined within the prescribed bound in the fixed time interval. Compared with the asymptotical consensus in infinite settling time, the proposed algorithm is less conservative. In addition, a new model transformation approach is presented to make the design results more advantageous in commonality. Simulation results show the effectiveness of the proposed controller, and reveal that the prescribed boundary of the disagreement trajectory has an effect on disturbance rejection performance.
In this paper, a hybrid time-varying delay projective synchronization method for a complex dynamical network is proposed using a hybrid feedback controller. The existing synchronous errors of the delay projective synchronization are constant. However, the synchronous errors of the time-varying delay projective synchronization show time-varying delay. The time-varying delay projective synchronization improves the delay projective synchronization and the different component variables of the system of the node achieve a different time-varying delay projective synchronization. This paper researches the hybrid time-varying delay projective synchronization of two different types of complex dynamical network. Numerical simulations are given to demonstrate the effectiveness of the proposed synchronization scheme.
The optimal tracking performance of networked control systems with communication delay and white Gaussian noise under the consideration of the channel input power constraint is studied in this paper. The tracking performance is measured by the energy of the system’s error signal and channel input power. The tracking performance is minimized by considering the channel input power constraint searching through all stabilizing two-parameter controllers. The optimal tracking performance of networked control systems under the communication delays, packet dropout and white Gaussian noise of the communication channel has been studied. The explicit expressions of the optimal tracking performance are obtained by applying the spectral factorization and inner–outer factorization techniques. It is shown that the optimal tracking performance depends on the non-minimum phase zeros, unstable poles of the given plant, communication delay, packet dropouts probability and white Gaussian noise. It is also shown that if the constraint of the communication channel does not exist, the optimal tracking performance reduces to the existing tracking performance of the control system without communication. The result shows how the communication delay, packet dropout probability and white Gaussian noise may fundamentally constrain a networked system’s tracking capability. Finally, some typical examples are given to illustrate the theoretical results.
Conventional disturbance observers for discrete-time linear stochastic systems assume that the system states are fully estimable and the disturbance estimate is dependent on the estimated system states, hereafter termed full-order disturbance observers (FODOs). This paper investigates the design of reduced-order disturbance observers (RODOs) when the system state variables are not fully estimable. An existence condition of RODOs is established, which is shown to be more easily satisfied than that of conventional FODOs and consequently it has substantially extended the scope of applications of disturbance observer theory. Then a set of recursive formulae for the RODO is developed for online applications. Finally, it is further shown that the conventional FODOs are a special case of the proposed RODO in the sense that the former reduces to the RODO when the states become fully estimable in the presence of disturbances. Examples are given to demonstrate the effectiveness and advantages of the proposed approach.
In this paper, the finite-time passive filtering problem of a class of neutral time-delayed systems is considered. The exogenous disturbances are unknown but norm bounded. A sufficient condition for passivity and finite-time stability of the combined system is derived and proved by means of Lyapunov functional methods and linear matrix inequalities (LMIs) techniques. The dynamic of the filtering error system is ensured to be finite-time bounded with a prescribed dissipation performance level
This paper is mainly devoted to a monotonically convergent iterative learning control (ILC) design for a class of uncertain discrete-time switched systems with state delay (UDTSDSs). By taking advantage of output error and state information, a hybrid ILC law for a class of UDTSDSs is proposed. After the ILC process is transformed into a 2D system, sufficient conditions in terms of linear matrix inequalities (LMIs) are derived by using a multiple Lyapunov–Krasovskii-like functional approach and a quadratic performance function. It is shown that if certain LMIs are met, the tracking error 2-norm converges monotonically to zero along the iteration direction, while the learning gains could be determined directly by solving the LMIs. The simulation results are provided to illustrate the theoretical analysis.
This work is oriented to research performance in terms of optimal transfer of energy from a photovoltaic generator to a permeant magnet synchronous motor type used as a centrifuge pump driver. The performance improvement is done using a variable structure controller with sliding mode as a maximum power point tracker (MPPT). A stability analysis for the proposed MPPT is developed for a photovoltaic (PV) pumping system. The global stability of the variable structure algorithm is demonstrated by means of Lyapunov’s approach. The tracking algorithm leads the PV generator coordinates to the maximum power point by changing the pulse width modulation (PWM) signal frequency of the boost converter. The steady-state behaviour of the PV pumping system with variable structure control is characterized by a stable oscillation around the maximum power point. The effectiveness of the proposed MPPT scheme is demonstrated under internal and external disturbances.
In this paper, a cascaded sliding mode control is designed for magnetic levitation systems usually comprised of an electrical loop and an electromechanical loop. A disturbance-observer-based sliding mode controller is designed for the electrical loop while a state-and-disturbance-observer-based sliding mode controller is designed for the electromechanical loop. The overall stability of the system is proved. The performance of the proposed scheme is compared with a conventional linear quadratic regulator combined with a proportional–integral controller by simulation as well as experimentation on a magnetic levitation setup in laboratory.
Down-hole oil and gas industry requirements for measuring thermodynamic and geophysical parameters, for instance pressure, temperature, vibration and multiphase flow, are challenging. Accomplishing these necessities requires a complete signal communications chain of high-performance components and effective signal processing communication techniques to provide system reliability. Nevertheless, noise interference, cable attenuation and thermal drift of the front-end passive electronic elements can lead to poor signal-to-noise ratio (SNR) and possibly loss of the communication link. This paper describes a signal processing algorithm implemented in a bidirectional communication system that exchanges data from a down-hole high pressure and high-temperature (HPHT) measurement tool to the surface installation. The communication channel is a multi-conductor coaxial logging cable also used as a power supply transmission line. The instrumentation system consists of a proprietary down-hole measurement tool, composed of an HPHT sensor and a high-temperature digital signal processor (DSP)-based electronic device; located in the surface installation is a data-acquisition equipment. The system employs a signal processing algorithm, based on the frequency domain SNR characterization of the whole communication chain, which determines in real time the optimal carrier frequency that is automatically implemented in the selected modulation/demodulation technique. The obtained laboratory test results of the down-hole tool, using changes in temperature from 25° to 185°C, provide a firm basis for testing and evaluating the system in the field.
Robust autopilot design for bank-to-turn (BTT) missiles under time-varying aerodynamic parameters, heavy nonlinear crossing couplings and external disturbances is investigated in this article by employing a distributed composite control framework. The dynamics of the BTT missiles are separated into three subsystems from the roll, yaw and pitch channels. For each subsystem, a composite control framework consists of a low-level state feedback stabilizer, a high-level model predictive controller and a feedforward compensator based on a disturbance observer. Simulation results demonstrate that compared with the baseline control and traditional integral control approaches, the proposed control strategy exhibits promising guidance command tracking and robustness against external disturbances, nonlinear crossing couplings and aerodynamic parameter perturbations.
This paper investigates cooperative tracking problems for multiple non-linear dynamic systems. The desired trajectory is only available to a portion of the agents. Treating the desired trajectory as a virtual agent, a terminal sliding mode (TSM)-based distributed control algorithm is designed only using the neighbours’ information. A decentralized observer is provided firstly to estimate the unknown external disturbances. Then, the Gaussian functions are introduced to approximate the second-order derivative of the inaccessible states. On this basis, we further develop a TSM-based cooperative tracking control algorithm for the non-linear dynamic systems such that the tracking errors of each agent converge to an adjustable neighbourhood of the origin in finite time. Finally, a simulation example is presented to illustrate the feasibility and effectiveness of the proposed approaches.
This paper proposes a new method to design an online robust adaptive dynamic programming algorithm (RADPA) for a wheeled mobile robot which is equipped with an omni-directional vision system. To integrate kinematic and dynamic controllers into the unique controller, we transform the strict feedback system dynamics into tracking error dynamics. Then, we propose a control scheme which uses only one neural network rather than three proposed in the actor-critic-based control schemes for the two-player zero-sum game problem. A neural network weight update law is designed for approximating the solution of the Hamilton–Jacobi–Isaacs equation without knowing knowledge of internal system dynamics. To implement the scheme, we propose the online RADPA, in which control and disturbance laws are updated simultaneously in an iterative loop. The convergence and stability of the online RADPA are proven by Lyapunov techniques. Simulations and experiments on a wheeled mobile robot testbed are carried out to verify the effectiveness of the proposed algorithm.
In this paper, an adaptive robust output feedback control approach is proposed for a class of uncertain non-linear systems with unknown input dead-zone non-linearity, unknown failures and unknown bounded disturbances. By constructing the dead-zone inverse and applying the backstepping recursive design technique, a robust adaptive backstepping controller is proposed, in which adaptive control law is synthesized to handle parametric uncertainties and a novel robust control law to attenuate disturbances. The robust output feedback control law is developed by integrating a switching function algorithm at each step of the backstepping design procedure. In addition, K-filters are designed to estimate the unmeasured states and neural networks are employed to approximate the unknown non-linear functions. By ensuring boundedness of the barrier Lyapunov function, the major feature of the proposed controller is that it can theoretically guarantee asymptotic output tracking performance, in spite of the presence of unknown input dead-zone non-linearity, various actuator failures and unknown bounded disturbances via Lyapunov stability analysis. The effectiveness of the proposed approach is illustrated by the simulation examples.
Because the non-linear and time-varying characteristics of the continuously variable transmission (CVT) system driven by using a six-phase copper rotor induction motor (IM) are unknown, improving the control performance of the linear control design is time consuming. To overcome difficulties in the design of a linear controller for the six-phase copper rotor IM servo-driven CVT system with lumped non-linear load disturbances, a blend modified recurrent Gegenbauer orthogonal polynomial neural network (NN) control system, which has the online learning capability to return to the non-linear time-varying system, was developed. The blend modified recurrent Gegenbauer orthogonal polynomial NN control system can perform overseer control, modified recurrent Gegenbauer orthogonal polynomial NN control and recompensed control. Moreover, the adaptation law of online parameters in the modified recurrent Gegenbauer orthogonal polynomial NN is based on the Lyapunov stability theorem. The use of amended artificial bee colony optimization (ABCO) yielded two optimal learning rates for the parameters, which helped improve convergence. Finally, comparison of the experimental results of the present study with those of previous studies demonstrated the high control performance of the proposed control scheme.
This paper presents a photovoltaic (PV) generator along with a battery energy storage system connected in series with a three-phase grid. The objective of the proposed system is to provide uninterruptable compensation to the series-connected grid and non-linear load during strong sunlight as well as at night or in cloudy conditions. The interface between the grid and the PV is carried out through a voltage source converter (VSC), eliminating both the current and voltage harmonics and compensating the reactive power. The DC voltage control of the DC bus capacitor is employed in order to maintain unity power factor operation of the system, irrespective of changes in solar radiation level or due to change in load. Another control scheme is implemented to charge and discharge the connected battery whenever the sun goes out, to meet the DC bus voltage requirement of the VSC through a bidirectional DC-DC converter.
This paper studies the distributed consensus tracking control problem of multiple uncertain non-linear strict-feedback systems under a directed graph. The command filtered backstepping approach is utilised to alleviate computation burdens and construct distributed controllers, which involves compensated signals eliminating filtered error effects in the design procedure. Neural networks are employed to estimate uncertain non-linear items. Using a Lyapunov stability theorem, it is proved that all signals in the closed-looped systems are semi-globally uniformly ultimately bounded. In addition, consensus errors converge to a small neighbourhood of the origin by adjusting the appropriate design parameters. Finally, simulation results are presented to demonstrate the effectiveness of the developed control design approach.
Consensus seeking problems are investigated for linear multi-agent systems in this paper. Two kinds of state observers, the decentralized Luenberger observer and the distributed pinning networked observer, are proposed to estimate the group agents’ state. Then, based on the observed state information, a novel distributed hybrid output feedback protocol is proposed. Using an eigenvalue analysis method, necessary and sufficient criteria have been established such that asymptomatic consensus can be achieved for a linear multi-agent system under a fixed directed communication topology. Furthermore, two multi-step algorithms are provided to determine the observer gains and the feedback gains for the proposed observer and distributed hybrid protocol. Finally, a numerical example is given to demonstrate the applicability and efficiency of the proposed consensus protocol.
In this paper, we discuss the speed regulation problem of permanent magnet synchronous motor (PMSM) servo systems. Firstly, a continuous terminal sliding mode control (CTSMC) method is introduced for speed loops to eliminate the chattering phenomenon while still ensuring a strong disturbance rejection ability for the closed-loop system. However, in the presence of strong disturbances, the CTSMC law still needs to select high gain which may result in large steady-state speed fluctuations for the PMSM control system. To this end, an extended state observer (ESO)-based continuous terminal sliding mode control method is proposed. The ESO is employed to estimate system disturbances and the estimation is employed by the speed controller as a feed-forward compensation for disturbances. Compared to the conventional sliding mode control method, the proposed composite sliding control method obtains a faster convergence and better tracking performance. Also, by feed-forward compensating system disturbances and tuning down the gain of the CTSMC law, the fluctuation of steady-state speed of the closed-loop system is reduced while the disturbance rejection capability of the PMSM system is still maintained. Simulation and experimental results are provided to demonstrate the superior properties of the proposed control method.
Rolling airframe manoeuvring is a type of manoeuvre in which the missile provides continuous roll during flight. Cross-coupling between the angle of attack and sideslip in rolling airframe missiles (RAMs) yields a coning motion around the flight path. As the pitch and yaw cross-coupling effect decreases, the radius of this coning motion decreases and the accuracy of the control system increases. Two-position (on–off) actuators are used in most RAMs. The presence of a two-position actuator in a feedback system makes its characteristics non-linear. A high-frequency signal so-called dither is applied to compensate for the non-linearity effect of the actuator characteristic in the feedback system and to stabilize the coning motion. The amplitude distribution function (ADF) method in dither analysis shows that the smoothed non-linearity characteristic can be computed as the convolution of the original non-linearity and the ADF of the dither signal. According to the four-degrees-of-freedom (4-DOF) equations of RAMs in a non-rolling frame and regarding various dither signals through the ADF approach on a two-position actuator, an analytical condition for dither amplitude in coning motion stability of RAMs is derived. It was shown that the triangular signal with specified amplitude and high enough frequency led to a smoother response of two-position actuators. Finally, by applying beam-riding guidance to a RAM, the performance of dithers for decreasing the distance of the missile from the centre of the beam is validated through simulations. It is illustrated that applying the triangular dither resulted in minimal error.
This paper considers the output tracking problem for micro-electro-mechanical systems (MEMS) under uncertainties and external disturbances. The robust non-linear controllers are designed by two methods. The first method consists of a backstepping strategy combined with a first-order sliding mode controller. Also, in order to reduce the chattering effect and to improve the robustness of the proposed scheme, a new variable universe fuzzy control action with an adaptive coefficient is used instead of the signum function in the switching control law. In the proposed fuzzy scheme, the centres of the output membership functions are optimized via three heuristic optimization algorithms including the artificial bee colony (ABC) algorithm, ant colony optimization (ACO) and particle swarm optimization (PSO). In the second method, a class of second-order sliding mode controller is combined with the backstepping strategy. The second controller includes the proposed optimal fuzzy controllers of the first method. The stability of the closed-loop systems in both approaches are proved via the Lyapunov stability criterion and the conditions of stabilization are provided by linear matrix inequalities (LMIs). Numerical simulations are carried out to verify the theoretical results and to demonstrate the robust performance of the proposed controller in output tracking of the time-varying reference signal.
This paper presents a weighing method for a truck scale based on a neural network (NN) with penalty function (PFNN). Firstly, the truck scale’s prior knowledge, i.e. the positive partial derivatives of the truck scale’s input–output function and the distributions of truck scale’s permissible weighing errors, is used to construct the constraints for optimizing the NN. Then, the penalty function is applied to construct the new NN’s performance index, and the detailed algorithm for training this NN is given. Finally, the method for assigning the value of the penalty factors is also discussed. The comparative experimental results show that the PFNN’s generalization ability is better than that of a data-induced NN (DINN) especially with a lack of training samples (the DINN is a method for training an NN only by using the data samples, not prior knowledge), and the weighing errors of the truck scale with PFNN are far less than those of DINN. In addition, the convergence of the PFNN is faster than that of the DINN.
In this paper, a sensor fault diagnosis approach is presented for a class of time delay non-linear systems via the use of adaptive updating rules. The considered system is represented by a time-varying delay dynamical state space model, and is subjected to a non-linear vector, which represents the modelling uncertainty in the state equation. Firstly, a fault detector observer is constructed to detect the fault. Then, the method for choosing the threshold value is given. Furthermore, a fault diagnosis device is constructed to diagnose the fault. The Lyapunov stability theory is used to obtain the required adaptive tuning rules for the estimation of the sensor fault. An adaptive diagnosis algorithm is developed to obtain information on the sensor fault. Finally, a simulated numerical example and a robotic example are included to demonstrate the use of the proposed approach, and experimental results show that the proposed adaptive diagnosis algorithm can track the fault signal and that the proposed method is valid.
An acceptance sampling plan plays a very important role in any quality assurance system. In this new economical design of acceptance sampling plan, three types of costs are included in the objective function by considering average outgoing quality limit (AOQL), average quality level (AQL) and lot tolerance percent defective (LTPD) constraints based on the maxima nomination sampling (MNS) method in a two-stage approach. The design of this sampling inspection plan involves the minimum average total inspection (ATI). The model is designed to minimize the summation of costs and the proposed MNS economical sampling plan is compared with the classical one. Practitioners can use the proposed model to decrease the total cost of inspection.
In this paper, the consensus problem for high-order discrete-time networked multi-agent systems (D-NMAS) is investigated by distributed feedback protocols. By constructing the self-feedback matrix and the neighbouring feedback matrix for networks, consensus protocols are designed under three different cases and the corresponding convergence analysis is provided. Consensus convergence results of networks are provided by three final consensus values, which are related to self-feedback matrices, initial states of networks and the topology of networks, not related to time delays. In the first case where a directed network with a fixed topology is concerned, the high-order discrete-time consensus problem is studied as an example, and a sufficient and necessary condition is obtained. In the scenario with directed networks with switching topology, a sufficient condition guaranteeing the consensus of high-order D-NMAS is derived, after the consensus analysis is transformed into stability analysis. As for directed networks with switching topology and time delays, the discrete-time stability model with time delays is converted into a general discrete-time stability model by an augmented method and the sufficient condition is provided to achieve consensus for directed networks. Furthermore, the sufficient conditions determining the neighbouring feedback matrix are independent of the number of agents. Two numerical examples are provided to demonstrate the correctness and effectiveness of the theoretical results.
In this paper, the problem of vehicle sideslip angle estimation is studied based on a single-track model, and an approach to robust H filter design with sampled-data measurements is proposed. Considering the changes of the vehicle mass, the moment of inertia about the yaw axis and the nonlinear relationships between the road surface and tyres, the vehicle lateral dynamics are characterized by a system with parameter uncertainties, which belong to a given convex polytope. By utilizing an input delay approach, the filtering error system is transformed into a continuous-time system with time delay in the state. By introducing a Lyapunov–Krasovskii functional and a free weighting matrix technique, LMI-based conditions have been formulated for the stability analysis of the filtering error system and the existence of admissible filters, which ensure the filtering error system is asymptotically stable with a prescribed H disturbance attenuation level. Finally, some simulation results are provided to illustrate the effectiveness of the proposed method.
This paper investigates the problem of designing a novel adaptive sliding-mode controller for heavyweight airdrop operations. The design objective is to guarantee asymptotic tracking performance of the aircraft states, in the presence of bounded nonlinear uncertainties without prior knowledge of the bounds. On the basis of feedback linearization of the aircraft–cargo model, a sliding-mode control method with projection-based adaptive function approximation is proposed. This method uses an adaptation strategy to achieve robustness against model uncertainties, and a knowledge of the bounds on the complex uncertainties is not required. Notably, the adaptation law with projection can bound the estimated function, and this avoids singularity of the control signal. Simulations are conducted under the condition that one transport aircraft performs a maximum load airdrop mission at a height of 25 m, using single-row single-platform mode. The results verify the good properties of the control method, which can meet the airdrop mission performance indexes well in the presence of ±20% aerodynamic data uncertainty and 20% actuator fault.
This article proposes a new practical robust attitude state feedback controller of a low-cost hexarotor micro aerial vehicle under the effects of noise in angular velocity measurements and multiple uncertainties (called the equivalent disturbance), which consist of external time-varying wind disturbance, nonlinear dynamics, coupling and parametric uncertainties. The proposed method is designed in two simple steps. Firstly, a nominal cascade controller is designed to reduce noise in angular velocity measurements and to achieve good attitude tracking performance in nominal conditions. Then, a second-order robust compensator is integrated into the closed-loop system to reduce the effects of the equivalent disturbance. The proposed control design is a linear time-invariant controller which is easily implemented in practical applications. Compared to other advanced attitude controllers, the proposed controller incurs lower computational costs and can easily be implemented in a low-cost embedded microcontroller system. In addition, a practical computational design procedure and an intuitive online tuning method for the proposed controller are presented in this article in order to provide a complete reference to micro aerial vehicle developers. The simulation and experimental results are presented to demonstrate the robustness of the proposed controller in operation in outdoor environments, to show good steady-state and dynamic tracking performance of the closed-loop system and to prove that the tracking errors are ultimately bounded within desired limits.
This paper is concerned with the identification and nonlinear predictive control approach for a nonlinear process based on a third-order reduced complexity, discrete-time Volterra model called the third-order S-PARAFAC Volterra model. The proposed model is given using the PARAFAC tensor decomposition that provides a parametric reduction compared with the conventional Volterra model. In addition, the symmetry property of the Volterra kernels allows us to further reduce the complexity of the model. These properties allow synthesizing a nonlinear model-based predictive control (NMBPC). Then we construct the general form of a new predictor and we propose an optimization algorithm formulated as a quadratic programming (QP) algorithm under linear and nonlinear constraints. The performance of the proposed third-order S-PARAFAC Volterra model and the developed NMBPC algorithm are illustrated on a numerical simulation and validated on a benchmark such as a continuous stirred-tank reactor system.
Biomedical applications of swimming microrobots comprising of drug delivery, microsurgery and disease monitoring make the research more interesting in MEMS technology. In this paper, inspired by the flagellar motion of microorganisms like bacteria and also considering the recent attempts in one/two-dimensional modelling of swimming microrobots, a three degrees-of-freedom swimming microrobot is developed. In the proposed design, the body of the swimming microrobot is driven by multiple prokaryotic flagella which produce a propulsion force through rotating in the fluid media. The presented swimming microrobot has the capability of doing three-dimensional manoeuvres and moving along three-dimensional reference paths. In this paper, following dynamical modelling of the microrobot motion, a suitable controller is designed for path tracking purposes. Based on the resistive-force theory, the generated propulsion force by the flagella is modelled. The feedback linearization method is applied for perfect tracking control of the swimming microrobot on the desired motion trajectories. It is seen that, by the use of three flagella, the microrobot is able to perform three-dimensional manoeuvres. From the simulation results, the tracking performance of the designed control system is perfectly guaranteed which enables the microrobot to perform the desired three-dimensional manoeuvres and follow the desired trajectory.
For the unsteady characteristics of a fault vibration signal of a wind turbine’s rolling bearing, a bearing fault diagnosis method based on variational mode decomposition of the energy distribution is proposed. Firstly, variational mode decomposition is used to decompose the original vibration signal into a finite number of stationary components. Then, some components which comprise the major fault information are selected for further analysis. When a rolling bearing fault occurs, the energy in different frequency bands of the vibration acceleration signals will change. Energy characteristic parameters can then be extracted from each component as the input parameters of the classifier, based on the K nearest neighbour algorithm. This can identify the type of fault in the rolling bearing. The vibration signals from a spherical roller bearing in its normal state, with an outer race fault, with an inner race fault and with a roller fault were analyzed. The results showed that the proposed method (variational mode decomposition is used as a pre-processor to extract the energy of each frequency band as the characteristic parameter) can identify the working state and fault type of rolling bearings in a wind turbine.
The four-wheel independent drive electric vehicle (4WID EV) has some advantages, such as independent control of torque, easy measurement of torque, and multiple drive modes, the most significant of which are four-wheel drive and two-wheel drive modes. However, there is a problem with the switched drive mode, which would have an adverse effect on the precision of vehicle velocity estimation, the vehicle stability and comfort. In order to solve the problem, a control strategy with a switched drive mode is proposed. The control strategy is based on two vehicle velocity estimation algorithms. Between the two vehicle velocity estimation algorithms, the vehicle velocity estimation algorithm based on an unscented Kalman filter is designed in a four-wheel drive mode condition, whereas the vehicle velocity estimation algorithm based on the wheel rotational speed is designed in a two-wheel drive mode condition. Switchover of the two vehicle velocity estimation algorithms would cause a vehicle velocity saltus step, which has an adverse effect on vehicle control, so a vehicle velocity smoothing algorithm is proposed. Simulation results show that the control strategy not only reaches a high vehicle velocity control accuracy, it also improves the vehicle stability as well as the comfort. Furthermore, the results show that the proposed strategy can achieve stabilization with disturbance.
The electric sail (ES) is a novel propellantless propulsion concept, which extracts the solar wind momentum by repelling the positively charged ions. Due to the difficulty of attitude adjustment by the large flexible structure and the uncertainty of ion density, velocity and electron temperature by solar wind, there exist thrust input uncertainty and saturation with time-varying bounds for ES. The trajectory tracking problem for ES in three-dimensional (3-D) space is studied, and the composite sliding mode control scheme with corresponding guidance strategy is proposed for the single-input–multiple-output (SIMO) non-linear system. The hierarchical sliding surfaces are constructed with an auxiliary design system to analyse the effect of input saturation constraints and decouple the SIMO non-linear system to reduce the control complexity. Also, the disturbance estimation based on a super-twisting algorithm is employed to decrease the switch chattering and improve the system robustness. It is proved that all the sliding mode surfaces are asymptotically stable, and all the signals of the closed-loop system are bounded with input saturation constraints. Furthermore, all the signals are converging to zero and the closed-loop system is asymptotically stable without saturation. Finally, the simulation demonstrates the proposed composite sliding mode control is fit for ES 3-D trajectory tracking.
This paper proposes two different path following control schemes for a stratospheric airship with actuator saturation. Each of the control schemes consists of a guidance loop and an attitude control loop. In both schemes, guidance laws are designed according to the line-of-sight guidance-based path following principle. In the first control scheme, a robust H controller without constraints is designed based on the planar model of a stratospheric airship to stabilize path-following errors. The input constraints are then addressed by using a regional
The design of an adaptive output feedback compensator is addressed to reject biased multi-sinusoidal disturbances of unknown amplitudes, phases and frequencies, acting on unknown stable single-input single-output linear systems of unknown order and relative degree. A single biased sinusoidal disturbance is first considered. It is shown that the knowledge of the sign of the real part (or the imaginary part) of its transfer function at zero and at the disturbance frequency is sufficient for the explicit design of an adaptive compensator which guarantees local exponential convergence to zero of the system output and of the frequency estimation error. This result is generalized to the case of biased multi-sinusoidal disturbances. As a special case, an adaptive notch filter which provides the unknown frequencies contained in the disturbance is obtained.
This paper considers the asymptotic stability of a class of nonlinear fractional order impulsive switched systems by extending the result of existing work. First, a criterion is given to verify the stability of systems by using the Mittag–Leffler function and fractional order multiple Lyapunov functions. Second, by combining the methods of minimum dwell time with fractional order multiple Lyapunov functions, another sufficient condition for the stability of systems is given. Third, by using a periodic switching technique, a switching signal is designed to ensure the asymptotic stability of a system with both stable and unstable subsystems. Finally, two numerical examples are provided to illustrate the theoretical results.
This paper proposes an effective hybrid discrete differential evolution (DDE) algorithm for solving a scheduling problem of flexible manufacturing systems (FMSs), where sequence-dependent setup times are considered. The objective is to find a deadlock-free schedule that minimizes the makespan. Based on the timed Petri net models of FMSs, a possible solution of the scheduling problem is represented as an individual that is a permutation with repetition of jobs. For the existence of deadlocks, most of the individuals cannot be directly decoded into feasible (live) schedules. Therefore, a deadlock controller is applied in the decoding scheme, and infeasible individuals are amended into feasible ones. Moreover, in order to overcome the premature convergence of DDE algorithm and improve solution quality, a variable neighbourhood search algorithm, which performs a systematic change of neighbourhood in solution searching, is adopted. Then a hybrid scheduling algorithm that combines a DDE with a variable neighbourhood search is presented. Computational results and comparison based on a variety of instances show the feasibility and superiority of the proposed algorithm.
In this paper, a robust decentralized control scheme is proposed for web-winding systems. The control input for each subsystem is divided into two parts, a reference control input and a control compensation. First, the reference control inputs and the error dynamic models are presented. Then, based on the error dynamic model, a decentralized controller is designed to compute the control compensation, and the relevant sufficient condition for the existence of the decentralized controller is derived in terms of linear matrix inequalities (LMIs). By virtue of regarding some parameters as interval variables, the proposed controller has good robustness with regard to parameter variations and is adapted to the changes of set point. Finally, a three-motor web-winding system is considered as an application example, and simulation and experimental tests illustrate the effectiveness of the proposed controller.
In this study, a novel fault diagnosis approach based on a non-linear spectrum feature is proposed for a multivariable non-linear system. The non-linear spectrum features are obtained using a non-linear output frequency response function (NOFRF) and kernel principal component analysis (KPCA). In order to improve the real-time performance of obtaining non-linear spectrum features, a frequency domain variable step size normalized least mean square (FVLMS) adaptive algorithm is presented to identify NOFRF. A multi-fault classifier based on the fusion of a support vector machine (SVM) is designed according to different frequency domain scales, and a fusion method by using sub-classifier classification reliability is proposed. A simulation example about a two-input–two-output non-linear system is provided to illustrate the effectiveness and performance of the proposed approach.
In this article, we have extended the design structures of dual auxiliary information-based control charts under a variety of sampling strategies and runs rules schemes. We have considered the cases of known and unknown skewed distributions by using the skewness correction (SC) method. The design structures under the skewness correction method are based on the degree of skewness of the study variable, amount of correlation between study variable and auxiliary variable, and sample size. We have investigated the performance of the developed structures in terms of probability of signals, false alarm rate and average run length by considering the symmetrical distribution, skewed distributions, heavy tailed distributions and contamination environments. Outcomes of the current article showed that control charts based on extreme ranked set strategies have higher probability of detecting an out-of-control signal and are comparatively more robust than other control charts, especially for known distributions. Furthermore, control charts for unknown skewed process distributions under extreme ranked set strategies are relatively more robust for a small sample size, followed by other ranked set strategies-based control charts for a large sample size. Moreover, we have included a real-life example for the monitoring of ground water variables to highlight the application of our proposals.
In this paper, a new reactive power and harmonic compensation procedure based on the variable forgetting factor-recursive least squares (VFF-RLS) estimator and new phase-locked loop is proposed. For this purpose, the fundamental harmonic component of load current is estimated with VFF-RLS. Independence on order and amplitude of harmonics, fast dynamic and high accuracy are the remarkable features of the designed estimator. As in the case of load changes, the sag/swell and phase jump in grid voltage, optimal compensation is performed in less than half a cycle with a low total harmonic distortion value. In addition, to compensate for an infected power system by varied frequency, a second-order general integrator-frequency-locked loop (SOGI-FLL) control system based on wavelet transform (WT) has been proposed. This phase detector can track the reference phase of a grid in the best manner in the presence of frequency variations. The effectiveness of the proposed method in a single-phase power system has been validated using both simulation and experimental results.
This paper proposes an adaptive smooth second-order sliding mode control law for a class of uncertain non-linear systems. The key point of this control law is ensuring a smooth control signal considering parametric uncertainty and disturbances with unknown bounds. The proposed control method is obtained by introducing a continuous function under the integral and using adaptive gains. The switching function and its derivative are forced to zero in finite time. This is achieved using a smooth control command and without the prior knowledge of upper bound parameters of uncertainties. The finite-time stability is proved based on a quadratic Lyapunov approach and the reaching time is estimated. This structure is used to create a homing guidance law and the efficiency is evaluated via simulations.
The deadlock control of flexible manufacturing systems (FMSs) has been widely studied in the literature. Petri nets (PNs) are extensively used as a tool for modelling, analysis and controller synthesis of such systems. In general, Petri-net-based liveness enforcing supervisors (LESs) include control places (CPs) together with their input/output arcs. It is well known that the methods proposed for computing CPs may provide redundant and necessary CPs. In this paper, a new method is proposed for redundancy test of CPs by means of supervisory control theory (SCT). The proposed method is based on the idea that after the removal of a CP from an LES, if the controlled model is still live, then the removed CP is redundant. The proposed method makes use of the TCT implementation tool of SCT. It is applicable to a PN-based LES consisting of a set of CPs. The applicability of proposed method is demonstrated by means of examples from the relevant literature. For some examples, the redundancy test provides more permissive behaviour with structurally simpler supervisors.
This paper focuses on the observer-based robust stabilization problem for a class of polynomial systems with norm-bounded time-varying uncertainties. The structural features of such systems guarantee the fulfilment of the separation principle between the reduced-order observer and the state feedback. The existence conditions of the observer-based robust stabilization controller are obtained by using Lyapunov stability theory. Furthermore, based on the polynomial sum of squares (SOS) theory, the above conditions are transformed into the corresponding SOS convex optimization constraints, for the avoidance of computing difficulties that exist widely in the control of non-linear systems. Finally, two numerical examples are given to illustrate the feasibility and effectiveness of the proposed approach.
A nonlinear sliding mode based scheme is developed for lateral guidance of unmanned aerial vehicles. The guidance and control system is considered as an inner and outer loop design problem, the outer guidance loop generates commands for the inner control loop to follow. Control loop dynamics is considered during derivation of the guidance logic, along with saturation constraints on the guidance commands. A nonlinear sliding manifold is selected for guidance logic design, the guidance loop generates bank angle commands for the inner roll control loop to follow. The real twisting algorithm, a higher order sliding mode algorithm is used for guidance logic design. Existence of the sliding mode along with boundedness of the guidance command is proved to ensure that controls are not saturated for large track errors. The proposed logic also contains an element of anticipatory or feed-forward control, which enables tight tracking for sharply curving paths. Efficacy of the proposed method is verified by flight testing on a scaled YAK-54 unmanned aerial vehicle. Flight results demonstrate robustness and effectiveness of the proposed guidance scheme in the presence of disturbances.
The paper is concerned with finite-time dissipativity analysis and design for stochastic Markovian jump systems with generally uncertain transition rates and time-varying delay. By constructing a more appropriate Lyapunov–Krasovskii functional, sufficient conditions for finite-time dissipativity of the underlying system are first proposed. Then, a state feedback controller is designed such that the closed-loop Markovian jump system is finite-time dissipative. These sufficient criteria are derived in the form of linear matrix inequalities (LMIs). Finally, numerical examples are given to demonstrate the validity of the main results.
In this paper, the problem of three-dimensional (3-D) system stability is studied. In order to investigate the stability of 3-D systems, a new representation scheme is introduced based on the local state model proposed by Givone–Roesser for 3-D systems. This representation is obtained from the extended expression of the 1-D wave model proposed by Porter–Aravena. Then, according to the obtained model a new criteria for the stability of 3-D systems is stated. This criteria provides a simpler way to investigate asymptotic stability. Furthermore, an algorithm is performed to illustrate the procedure of analysing stability. Finally, some examples are performed and verified using numerical simulations in order to illustrate the given criteria for the stability.
In wind energy generation, self-excited induction generators (SEIGs) are playing a vital role in isolated areas where the extension of the grid is not feasible. But the major problem with such generators are their inability to maintain the terminal voltage and frequency constant with the load. Many SEIG systems require the computation of peak voltage and frequency for the subsequent feeding to a voltage and frequency controller for processing. In this paper, a new technique for computing the peak voltage of a SEIG using the COordinate Rotation DIgital Computer (CORDIC) algorithm is proposed. The proposed peak voltage estimation scheme requires only one voltage sensor. The SEIG voltage and frequency is controlled using a generalized impedance controller. To validate the simulation results obtained from MATLAB/Simulink, an experiment is also carried out using a TMS320F2812 DSP processor.
The electrocardiogram (ECG) is an important technique for heart disease diagnosis. This paper proposes a novel method for ECG beat classification. Several important issues exist in the ECG beat classification, which, if suitably addressed, may lead to development of more robust and efficient recognizers. Some of these issues include feature extraction, choice of classification approach and optimization. A new method for non-linear feature extraction of ECG signals based on empirical mode decomposition (EMD), approximate entropy (ApEn) and wavelet packet entropy is presented. The proposed method first uses EMD to decompose ECG signals into a finite number of intrinsic mode functions (IMFs), calculates the ApEn of IMFs as one feature and then obtains the wavelet packet entropy of wavelet packet coefficients as another feature. The two features are regarded as a feature vector. The support vector machine (SVM) and probabilistic neural network (PNN) are used for beat classification. The particle swarm optimization algorithm is used to optimize parameters of the PNN and SVM. The performance of the SVM classifier is slightly superior to that of the PNN classifier with 98.6% accuracy.
Domestic air conditioners are a major source of energy consumption. In this study, utilizing real-time data from a public domain, a cascaded hardware in loop approach to the control of room temperature is considered. An inner loop to control the supply air temperature by adjusting the electronic expansion valve using a second-order plus delay time model is proposed. The room temperature control is considered the outer loop. A simplified lumped parameter representation of the thermal dynamics of the building is modelled in MATLAB using ordinary differential equations. A constrained multi parametric model predictive controller (mpMPC) is designed for both the control loops. The constraints include safety limits on the superheat and manipulation rates for the inner loop and a rate constraint on the reference signal in the outer loop. Model uncertainties like ambient temperature and thermal load variations (representing an office space) are considered for hardware in the loop testing of the proposed strategy. From performance analysis, using power spent and thermal comfort quantization, it is observed that the mpMPC scheme outperforms traditional control strategies.
A strategy to regulate unstable processes using a modified Smith predictor based sliding mode controller (SP-SMC) is illustrated. The proposed scheme presents disturbance rejection and optimal control input usage with overall improved regulatory performances. The unstable process with time delay is first estimated using a simple measurement of limit cycle output obtained from a modified relay experiment. Then this paper extends a work on SP-SMC for unstable processes, which leads to significant improvements in its regulatory capacities of reference inputs and disturbances. A new control law is incorporated in the discontinuous part of sliding mode control such that the overall performance improves significantly. The metaheuristic search algorithm with some modifications has been implemented successfully to satisfy the new control performance index. The robustness of the controller is also tested under the process uncertainty. Illustrative examples show the simplicity and superiority of the presented design method over previously published approaches.
In this paper, a new form of the Bouc–Wen model is identified. This model, which includes hysteresis loop, is suitable to use in controller design. The modified Bouc–Wen model is employed as an experimental model to identify the new model. Evaluation of the proposed model illustrated that this model has good accuracy in small velocity area. A novel approach of model predictive control is employed to derive the control input or magneto-rheological damper current, with respect to constraints. This controller gives analytical control law by means of the Karush–Kuhn–Tucker (KKT) optimization method in order to solve the constrained optimization problem. One of the main advantages of this controller is its analytical form, which allows easy implementation of the controller, and this special form leads to rapid calculation of the control input. Simulations show that the proposed control strategy establishes an appropriate trade-off between suspension criteria, including ride comfort and road holding.
This paper deals with the low-gain output feedback cooperative control for discrete-time linear multi-agent systems subject to actuator saturation. Based on the algebraic Riccati equations, semi-global exponential consensus is demonstrated for multi-agent system networks with a switching topology that is known or unknown to the agents. The combinational measurements are utilized to design the state observer. The estimated combinational states are used for controller design and Lyapunov stability analysis. The theoretical results are illustrated by a numerical example.
The input–output finite time stability (IO-FTS) for a class of fractional order linear time-invariant systems with a fractional commensurate order 0 < α < 1 is addressed in this paper. In order to give the stability property, we first provide a new property for Caputo fractional derivatives of the Lyapunov function, which plays an important role in the main results. Then, the concepts of the IO-FTS for fractional order normal systems and fractional order singular systems are introduced, and some sufficient conditions are established to guarantee the IO-FTS for fractional order normal systems and fractional order singular systems, respectively. Finally, the state feedback controllers are designed to maintain the IO-FTS of the resultant fractional order closed-loop systems. Two numerical examples are provided to illustrate the effectiveness of the proposed results.
The attitude control problem is much more complicated due to the existence of faults and multiple disturbances simultaneously in satellites. In this paper, an adaptive fault tolerant attitude control method is presented for satellites with both time-varying actuator faults and multiple disturbances. Differing from previous results, the first part of the multiple disturbance is the uncertain modelled disturbance and the second part represents an uncertain variable bounded by a given function. A composite observer is designed to estimate the uncertain modelled disturbance and the time-varying fault simultaneously. Then, a new fault tolerant control strategy consisting of disturbance observer based control, fault accommodation and an adaptive controller is constructed to reconfigure the concerned systems and so as to achieve the anti-disturbance performance. In the proposed method, the modelled disturbance can be rejected by its estimation and the uncertain bounded disturbance can be compensated by the adaptive compensation term. The simulation results are given to show the efficiency of the proposed approach.
A new anti-disturbance control scheme is proposed for a class of nonlinear systems subjected to multi-source disturbances. The uncertain multi-source disturbances are classified into two parts; one is the harmonic and constant disturbance with partial known information, which can be formulated by an exogenous system, and the other consists of unknown time-varying disturbances. The nonlinear disturbance observer (NDO) is constructed to estimate the first kind of disturbance. By integrating the NDO with an adaptive backstepping technique, a novel composite anti-disturbance control scheme is proposed for the nonlinear systems. Then the stability analysis of the closed-loop system is presented. The simulation of a numerical example is given to demonstrate the effectiveness of the proposed results.
When rotor rubbing occurs, the vibration signal comprises a periodic signal, a transient impact signal and noise. The main components of the periodic signal are the rotating frequency and harmonics thereof. The transient impact signal includes the rotor fault information. According to the characteristics of the local rub-impact fault in a rotor system, an adaptive local iterative filtering (ALIF) method is applied to fault diagnosis of the rotor local rub-impact. The ALIF method is used to decompose rotor vibration signals and can separate the rub, background and noise signals. The fault features of rotor local rub-impact can be extracted from the rotor vibration signal. A case study showed that the ALIF method can be effectively applied to the fault diagnosis of rotor local rub-impact.
The study of changes in physiological signals for emotion recognition in human subjects has generated immense interest in medical instrumentation. One of the effective ways of classifying emotions is by the use of the event-related potentials (ERPs) of electroencephalogram (EEG) signals. This requires projection of an image on one computer system while simultaneously putting a marker on another computer system acquiring the EEG. This is achieved using costly modules to synchronize the stimulus presentation system with the data acquisition system. This paper describes an innovative low-cost technique to achieve this simultaneous triggering on the second computer system using the parallel operation of mechanical keyboards. The latency aspect of both USB and PS/2 keyboards with their two keys galvanically connected have been experimentally analysed and compared. It has been found experimentally that the USB keyboards, if used in cognition research, would give better latency results compared with the PS/2 keyboards. The synchronization error between the two USB keyboards has been found to be lower than or equal to 1 ms for the maximum number of keystrokes. The horizon of applications of this technique is unlimited and it can be used in almost every sphere of cognition enhancement and research, where a perfect synchronization of the brain stimulus with the corresponding physiological signals being acquired is required.
A new approach incorporating adaptive lighting intensity for micro-crack inspection of solar wafers with variable thickness is proposed. Wafer thickness is measured with a pair of laser displacement sensors and the lighting intensity is adaptively adjusted to normalize near infrared (NIR) transmission based on measured thickness. This technique enables the image contrast be maintained at relatively uniform intensity in response to the variation of the solar wafer thickness. An improved version of Niblack segmentation algorithm is developed for this application. Experimental results demonstrate the competitiveness of the proposed system compared with established techniques, and achieves better performance both visually and quantitatively. Meanwhile, the runtime is consistently less than 1 s, corresponding to a throughput rate of approximately 3600 wafers/h. These results suggest that the methods and procedures are suitable for online processing of solar wafers.
This paper investigates a novel anti-disturbance speed tracking control problem for permanent magnet synchronous motor (PMSM) systems with unknown mismatched disturbances. In order to realise the rejection and compensation for load torque, a cascaded PMSM system is constructed by using a coordinate transformation such that the load disturbances become matched with respect to the virtual control input. By combining disturbance observer with proportional-integral feedback control structure, a composite speed controller is proposed on this basis to ensure the PMSM system stability, and convergence of the tracking error of angular velocity to zero. The favourable observation performance for the load torque and its derivative can also be achieved simultaneously. Meanwhile, the
The acceleration response of a servo-hydraulic shaking table to a sinusoidal input motion is inevitably distorted by parasitic harmonic content caused by inherent non-linearities within the system. Herein, an algorithm is developed to characterize these parasitic motions in order to facilitate harmonic cancellation. The proposed algorithm is based on particle swarm optimization (PSO). Optimization is achieved by each particle’s movement, updated by its local best-known position and global best-known position according to a fitness function, which itself is a function of the estimation error between the identified acceleration and the original acceleration response. The estimation scheme is validated by experiments on a servo-hydraulic shaking table and results are compared with a more traditional harmonic analysis.
To achieve the radial displacement self-sensing detection of a bearingless induction motor, an observation method based on the LS-SVM (least squares support vector machine) is proposed. The state-space model of a magnetic suspension system is derived firstly. Then the LS-SVM is introduced to the radial displacement observer of the bearingless induction motor, the design principle and Lyapunov stability of the LS-SVM displacement observer are analysed in detail, and the construction method of the LS-SVM displacement observer is presented also. Simulation and verification results show that both in the starting suspension stage and in the process of stable suspension operation, the LS-SVM displacement observer can quickly track the radial displacement with high accuracy. Then, a new method is found for the radial displacement self-sensing detection of the bearingless induction motor.
Considering the deficiencies of the Co-training algorithm including redundancy and sufficient conditions, diversity measurers of classifier, combined label rules, unoptimizable classifier during model updating process etc., a multi-view collaborative semi-supervised classification algorithm based on diversity measurers of the classifier with the combination of agreement and disagreement label rules (Co-AgDiag) is proposed. This algorithm uses a combination of agreement and disagreement label rules during the labelling unlabelled data process by judging whether the two classifiers are consistent and considering the diversity threshold in order to improve the performance of the classifier. During the process of generating classifier and model updating, we employed the diversity rule of classifier to update the model in order to judge the performance of the two classifiers. The experimental results on UCI datasets demonstrate the effectively and feasibility of the proposed algorithm for multi-view classification problems.
The problem of adaptive finite-time control is addressed in this paper for a class of non-linear delay systems. First, the concepts of adaptive finite-time stability and adaptive finite-time boundedness are defined, respectively. Then, by resorting to the Lyapunov–Krasovskii functional technique, some new delay-dependent criteria guaranteeing adaptive finite-time boundedness and adaptive finite-time stability are developed, respectively. An explicit expression for the desired non-fragile state feedback controller is also presented. Finally, a numerical example is provided to demonstrate the effectiveness of the proposed results.
In this paper, by using a distributed control system (DCS) device, a robust tracking control system is proposed based on robust right coprime factorization for a heat exchanger actuated by a water level process with coupling effects and uncertainties. Firstly, nonlinear models of water level and temperature processes with coupling and uncertainties are given. Secondly, nonlinear feedback tracking control systems corresponding to a multi-input multi-output (MIMO) process are realized by using operator-based robust right coprime factorization. Meanwhile, stability of the control systems is guaranteed by using robust stability conditions compatible with the MIMO process including coupling effects, and to improve the output tracking performance, tracking controllers are designed. Finally, the effectiveness of the proposed design scheme is confirmed by simulation and experimental results.
In this paper, the problem of chaos suppression for a four-dimensional fundamental power system (FDFPS) model is considered via the design of a novel adaptive feedback controller. The period doubling bifurcation route to chaos and some dynamical behaviors of the power system are investigated in detail. Based on stability analysis using an energy-type Lyapunov function, a single adaptive feedback controller is derived to suppress chaotic oscillation in four-dimensional fundamental power systems. The proposed controller simplifies the design of power system stabilizer and provides an easy way to implement in practical power system control. In addition, effectiveness of damping out chaotic oscillation and robustness against parameter uncertainty and external disturbance also make the proposed control scheme applicable for industrial application. Simulation results illustrate the effectiveness, the robustness and the superiority of proposed control method.
In this paper, a novel method is constructed for model predictive control (MPC) of multi-input multi-output (MIMO) systems. The latter are represented by a discrete-time MIMO ARX model expansion on Laguerre orthonormal bases. The resulting model, entitled the MIMO ARX-Laguerre model, provides a recursive representation with parameter number reduction. This reduction is strongly linked to the choice of Laguerre poles, and therefore we propose a new algorithm to optimize the Laguerre poles of the resulting model. The recursive formulation of the MIMO ARX-Laguerre model is used to obtain the MPC strategy. An
There are many methods for designing feedback control systems with the objectives of disturbance rejection and reference following. Two well-known methods are to directly embed a disturbance model in the controller or to compensate for the effect of disturbance using its estimation from an observer. It is demonstrated in this paper that the first method has inherited a two-degree-of-freedom control system configuration, but has an integrator windup problem in the presence of actuator saturation, whilst the second approach has embedded an anti-windup mechanism, but suffers from poor performance in reference following. This paper examines these two mainstream control design methods for their applications in the presence of actuator saturation. It is shown with a simple example that the classical control system by directly embedding a disturbance model leads to integrator windup, and an anti-windup mechanism is hence proposed to overcome this drawback. The frequency response analysis presented in this paper leads to the conclusion that both methods offer similar closed-loop performance in terms of disturbance rejection and noise attenuation, however, the first approach that embeds a disturbance model in the controller provides a two-degree-of-freedom control system configuration, and hence gives a better performance in terms of reference following. Comparative simulation studies of both Single-Input and Single-Output (SISO) and Multi-input and Multi-Output (MIMO) systems are used to support the conclusions.
In this paper, the problem of robust stability and tracking of saturated control systems for buck DC-DC converters is considered. Linear Matrix Inequalities (LMIs) are used to insert the constraints in the design phase while imposing positivity in the closed-loop state. The control objective is to design a control law for the converter that limit duty ratio between 0 and 1. This will allow the system to switch between two topologies in the continuous conduction mode (CCM), to achieve a tracking reference condition. This has been developed using uncertain saturated control and regional pole placement techniques. The proposed controller is applied to a real DC-DC buck converter through a Hardware-In-the-Loop (HIL) test system. Experimental and simulation results show the effectiveness and the success of the proposed controller in tracking a reference voltage with limiting the duty ratio between 0 and 1. Results also show that the proposed controller performed better than the conventional one.
In this paper, the problem of finite-time boundedness of switched systems in the presence of both discrete and distributed delays is addressed. The multiple Lyapunov–Krasovskii functional approach is proposed to give some criteria ensuring that the state trajectories of the system remain bounded within a finite time interval. The switching law is designed in terms of average dwell time technique and the Jensen inequality. To further reduce the conservatism, we adopt the Wirtinger inequality which encompasses the Jensen one to derive a new condition. Finally, two numerical examples are presented to demonstrate the effectiveness of our result and the potential of employing the Wirtinger inequality.
In an electro-hydraulic system (EHS), the throttling phenomenon of the hydraulic valve leads to the problem of low utilization efficiency of hydraulic energy and severe increases in temperature. To alleviate this problem, this paper presents a type of one-chamber-controlled hydraulic circuit. In some applications where elastic load is dominant, this hydraulic circuit can achieve a significant energy-saving effect. For a valve-controlled system, the orifice non-linearity and the slowly varying parameter significantly influence the control performance of the electro-hydraulic system. With this aim in mind, the orifice compensation method is proposed to deal with the orifice non-linearity. Based on the compensation, the fractional order proportional–integral (FOPI) controller is adopted to deal with the problem of fluid parameter variation. In the controller designing process, this paper proposes a controller parameter tuning method based on system frequency characteristic data. Simulation and experiment results show that the strategy presented in this paper can reduce the energy losses dramatically and, at the same time, the control performance of electro-hydraulic system can be guaranteed.
In this paper, a new intelligent control scheme based on multiple models and neural networks is proposed to adaptively control a class of Hammerstein nonlinear systems with arbitrary deadzone input. This approach consists of a linear robust adaptive controller, multiple neural networks-based nonlinear adaptive controllers and a switching mechanism. Since the control input is derived from a modified certainty equivalent principle, the manner in which the closed-loop stability is established forms the main contribution. To show the usefulness of the developed results, three simulation examples, including a direct current motor subject to a nonlinear friction, are studied.
This paper proposes two different adaptive robust sliding mode controllers for attitude, altitude and position control of a quadrotor. Firstly, it proposes a backstepping non-singular terminal sliding mode control with an adaptive algorithm that is applied to the quadrotor for free chattering, finite time convergence and robust aims. In this control scheme instead of regular control input, the derivative of the control input is achieved from a non-singular terminal second-layer sliding surface. An adaptive tuning method is utilized to deal with the external disturbances whose upper bounds are not required to be known in advance in the inner loop. Secondly, a nonlinear disturbance observer based on the integral sliding mode with adaptive gains is proposed for position control, which is known as the outer loop. Stability and robustness of the proposed controller are proved by using the classical Lyapunov criterion. The simulation results demonstrate the validation of the proposed control scheme.
The landing safety and accuracy of a Mars lander will be seriously degraded due to multiple unknown disturbances or perturbations in the powered descent phase. A novel composite guidance algorithm is proposed to improve the landing performance in this paper. The presented guidance algorithm, with a composite hierarchical framework, is developed by the combination of disturbance observer-based control and multiple sliding surfaces guidance theory. The major disturbance owing to the Mars wind could be estimated through a disturbance observer and incorporated in the feed-forward compensation in the inner loop, other disturbances or perturbations could be attenuated by the multiple sliding surfaces technique in the outer loop. The composite guidance algorithm could be utilized without a pre-computed reference trajectory and it has enhanced anti-disturbance capability compared with the multiple sliding surfaces guidance law. Its global stability is verified using a Lyapunov-based approach. Monte Carlo simulation results show that the composite guidance law has a better performance on guiding a Mars lander from the point of engine ignition to the desired landing point in the presence of disturbances and perturbations.
This paper deals with developing a robust iterative algorithm to find the least-squares (P, Q)-orthogonal symmetric and skew-symmetric solution sets of the generalized coupled matrix equations. To this end, first, some properties of these type of matrices are established. Furthermore, an approach is offered to determine the optimal approximate (P, Q)-orthogonal (skew-)symmetric solution pair corresponding to a given arbitrary matrix pair. Some numerical experiments are reported to confirm the validity of the theoretical results and to illustrate the effectiveness of the proposed algorithm.
This paper studies a multi-objective mixture inventory problem for a pharmaceutical distributor. The work starts with a discussion of a mixture inventory model and three objectives, namely the minimization of: 1) ordering and holding costs, 2) number of units that stockout and 3) frequency of stockout occasions. Multi-objective particle swarm optimization (MOPSO) is used to determine the non-dominated solutions and generate Pareto curves for the inventory system. Two variants of MOPSO are proposed, based on the selection of inertia weight. The performance of the proposed MOPSO algorithms is evaluated in comparison with two robust algorithms like non-dominated sorting genetic algorithm II (NSGA-II) and multi-objective cuckoo search (MOCS). The metrics that are used for the performance measurement of the algorithms are error ratio, spacing and maximum spread. Furthermore, the technique of order preference by similarity to ideal solution (TOPSIS) is used to rank the non-dominated solutions and determine the best compromise solution among them. A factorial analysis develops the linear regression expressions of optimal cost, service level measures, lot size and safety stock factor for practitioners. Lastly, the results of the regression equations are compared using a MOPSO–TOPSIS approach and the validity of the developed equations are checked.
In the past decade, there has been a significant increase in the use of power electronic components in the design of household and industrial equipment. The use of power electronic based renewable energy resources, electric vehicles and other residential nonlinear loads may result in significant increases in injection levels of harmonics across a power system. Hence, it is important for utility companies to ascertain the exact harmonic levels present in terms of the amplitude and phase of each harmonic order. This paper provides a mathematical basis for distribution system state-space equations to formulate an iterative observer, which can simultaneously estimate harmonics present in a number of measurements taken from the power system. The method not only improves the computation time and provides real-time data for harmonic monitoring, but also performs wide area harmonic estimation for harmonic observability. Simulations and comparisons are provided to illustrate the performance of the proposed method against that obtained using a Kalman filter and fast Fourier transform (FFT). A number of scenarios such as measurement noise and change in amplitude of harmonic injections are simulated to verify the accuracy of the proposed approach and the results are included.
This paper presents a new method for control of linear networked systems by combining the predictive control and the variable sampling period approaches. In this way, event-driven sensors are implemented, i.e. by construction the sensors are triggered to sample the outputs of the plant, when new control input signals are received by the actuators. In light of this formulation, at each sampling instant, total control loop delay will be equal to the sampling period which is unknown. In order to deal with the network effects associated with a range of pre-specified time delays, appropriate step invariant discrete-time models of the networked plant are calculated offline. Based on these, some stabilizing control signals are constructed online. The control signals are then packed in the control-side packet, transmitted back to the plant side and received by a time delay compensator module. A less conservative class of Lyapunov functions, called switched quadratic Lyapunov, is used here for stability analysis and stabilizing controller design. Simulation studies on well-known benchmark problems demonstrate the effectiveness of the proposed method.
In this paper, a theoretical comparison between existing the sigma-point information filter (SPIF) framework and the unscented information filter (UIF) framework is presented. It is shown that the SPIF framework is identical to the sigma-point Kalman filter (SPKF). However, the UIF framework is not identical to the classical SPKF due to the neglect of one-step prediction errors of measurements in the calculation of state estimation error covariance matrix. Thus SPIF framework is more reasonable as compared with UIF framework. According to the theoretical comparison, an improved cubature information filter (CIF) is derived based on the superior SPIF framework. Square-root CIF (SRCIF) is also developed to improve the numerical accuracy and stability of the proposed CIF. The proposed SRCIF is applied to a target tracking problem with large sampling interval and high turn rate, and its performance is compared with the existing SRCIF. The results show that the proposed SRCIF is more reliable and stable as compared with the existing SRCIF. Note that it is impractical for information filters in large-scale applications due to the enormous computational complexity of large-scale matrix inversion, and advanced techniques need to be further considered.
Traditional underwater vehicles are limited in speed due to dramatic friction drag on the hull. Supercavitating vehicles exploit supercavitation as a means to reduce drag and increase their underwater speed. Compared with fully wetted vehicles, the non-linearity in the modelling of cavitator, fin and in particular the planing force make the control design of supercavitating vehicles more challenging. Dominant non-linearities associated with planing force are taken into account in the model of supercavitating vehicles in this paper. Two controllers are proposed to realize stable system dynamics and tracking responses, a linear quadratic regulator (LQR) control scheme and a robust backstepping control (RBC) scheme. The proposed backstepping procedure, in association with integral filters technique, exploits the possibility of avoiding the overparameterization problem existing in the classical backstepping process. In particular, the achieved stability is robust to modelling errors in supercavitating vehicles. Compared with the LQR control scheme, the RBC scheme is seen to increase the robustness with saturation compensation algorithm, which can be useful for avoiding actuator saturation in magnitude.
This paper presents simulation and real-time implementation of input-shaping schemes with a distributed delay for control of a gantry crane. Both open-loop and closed-loop input-shaping schemes are considered. Zero vibration and zero vibration derivative input shapers are designed for performance comparison in terms of trolley position response and level of sway reduction. Simulation and experimental results have shown that all the shapers are able to reduce payload sway significantly while maintaining satisfactory position response. Investigations with different cable lengths that correspond to ±20% changes in the sway frequency have shown the distributed delay-based shaper has asymmetric robustness behaviour. The shaper provides highest robustness for the case of 20% increase in the sway frequency but lower robustness for the case of 20% decrease. However, other schemes give symmetric robustness behaviour for both cases.
In a photovoltaic (PV) system, maximum power point tracking (MPPT) under partial shading (PS) conditions is a challenging task due to the presence of multiple peaks in the power voltage characteristics. This paper puts forward a novel artificial fish-swarm algorithm (FSA), which is optimized by particle swarm optimization with extended memory (PSOEM-FSA). In this algorithm, both the velocity inertia factor and the memory and learning capacity of PSOEM are introduced into the FSA. To validate the effectiveness of the novel algorithm, the PV system along with the proposed MPPT algorithm was simulated using Matlab/Simulink Simscape tool box. The simulation results show that the proposed approach is effective in MPPT under PS conditions and has a more stable performance when compared with the traditional methods in convergence speed and searching precision.
The semi-global output regulation problem of multi-variable discrete-time singular linear systems with input saturation is investigated in this paper. A composite nonlinear feedback control law is constructed by using a low gain feedback technique for semi-global stabilisation of discrete-time singular linear systems with input saturation. The sufficient solvability conditions of the semi-global output regulation problem by composite nonlinear feedback control are established. When the composite nonlinear feedback control law is reduced to a linear control law, the solvability conditions are an exact discrete-time counterpart of the semi-global output regulation problem of continuous-time singular linear systems. With the extra control freedom of the nonlinear part in the composite nonlinear feedback control law, the transient performance of the closed-loop system can be improved by carefully choosing the linear feedback gain and the nonlinear feedback gain. The design procedure of the composite nonlinear feedback control law and the improvement of the transient performance are illustrated by a numerical example.
A sliding mode observer (SMO) is proposed for the estimation of cylinder pressure using crankshaft speed fluctuations. SMO parameters are updated using the difference between measured and observed crankshaft speed. The governing equations of cylinder pressure and crankshaft speed are described by a one-zone combustion model and crankshaft model dynamics, respectively. The observer is found to be unstable at top dead centre (TDC) due to zero combustion torque at this point. To prevent instability, observer gains are switched off near TDC and observer performance depends only on the modelling accuracies of the combustion model. This model is further improved by modelling flame development angle
In most traditional electrocardiogram (ECG) detection procedures, wet electrodes must be glued to the skin during the procedure and may cause problems such as inconvenience and skin irritation. Furthermore, the quality of the acquired signals decreases because the glue dehydrates over time. In this study, a non-contact ECG acquisition system based on capacitive coupling textile electrodes with low-power consumption and high input impedance is presented. We designed electrodes that have a composite and textile structure. A kind of conductive textile with stainless steel wire creates these electrodes. We wove the conductive textile that has good electrical conductivity with a surface resistivity of 1.25 /sq. Both circuit models of the skin–electrode interface and amplifier for the capacitively coupled textile electrode were established, and the output signal-to-noise ratio (SNR) of the front-end circuit was proposed. The integrated system combines amplification, filter circuit and analogue-to-digital converter. The final measurement shows that the ECG signals acquired by our system are adequate for heartbeat detection and applicable to clinical practice.
The main goal of the present research is to deal with two non-identical hyperchaotic master–slave systems based on an efficient adaptive sliding mode control algorithm, namely the adaptive sliding mode control approach. As long as both non-identical systems are synchronized, the systems mentioned need to be handled through the proposed control algorithm. Since the effect of the external disturbance and uncertainty may truly be ignored, the whole of the chosen states of the slave system should be followed by the corresponding states of the master system in a very careful manner. The proposed adaptive sliding mode control approach has been realized to cope with the synchronization error, in a reasonable amount of time, in order to drive the applicable hyperchaotic systems. Finally, the performance of the proposed control scheme is verified via the simulation results.
This paper introduces differential graphical games for continuous-time non-linear systems and proposes an online adaptive learning framework. The error dynamics and the user-defined performance indices of each agent depend only on local information and the proposed cooperative learning algorithm learns the solution to the cooperative coupled Hamilton–Jacobi equations. In the proposed algorithm, each one of the agents uses an actor/critic neural network (NN) structure with appropriate tuning laws in order to guarantee closed-loop stability and convergence of the policies to the Nash equilibrium. Finally, a simulation example verifies the effectiveness of the proposed approach.
In this paper, a composite anti-disturbance autopilot design for a missile system is proposed based on finite time integral sliding mode control scheme and nonlinear disturbance observer technique. First, a nonlinear disturbance observer is developed to estimate the external disturbances with partial known information. Second, in order to obtain good disturbance rejection performance and reject the other type of disturbances, a finite time integral sliding mode control strategy is employed to the design of a feedback controller. Third, an improved adaptive composite anti-disturbance autopilot is given to avoid the inaccuracy of the upper bound of external disturbances. Finally, simulation results are employed to demonstrate the effectiveness of the proposed method.
This paper addresses a non-myopic sensor-scheduling problem of how to select and assign active sensors for trading off the tracking accuracy and the radiation risk, where the radiation risk is incurred by the fact that the emission energy originating from active sensors for target tracking can be intercepted by the enemy target. This problem is formulated as a mixed partially observable Markov decision process (POMDP) composed of a continuous-state POMDP for target tracking and a discrete-state POMDP for emission control. Based on the idea of foresight optimization, the long-term accuracy reward is evaluated by the combination of unscented transformation sampling and Kalman filtering, whereas the long-term radiation cost is derived from hidden Markov model filter. Because the problem can be converted into a decision tree, a branch and bound algorithm is developed for problem solution. A simulation example illustrates the effectiveness of our approach.
Modelling of non-linear dynamics of an air manifold and fuel injection in an internal combustion (IC) engine is investigated in this paper using the Volterra series model. Volterra model-based non-linear model predictive control (NMPC) is then developed to regulate the air–fuel ratio (AFR) at the stoichiometric value. Due to the significant difference between the time constants of the air manifold dynamics and fuel injection dynamics, the traditional Volterra model is unable to achieve a proper compromise between model accuracy and complexity. A novel method is therefore developed in this paper by using different sampling periods, to reduce the input terms significantly while maintaining the accuracy of the model. The developed NMPC system is applied to a widely used IC engine benchmark, the mean value engine model. The performance of the controlled engine under real-time simulation in the environment of dSPACE was evaluated. The simulation results show a significant improvement of the controlled performance compared with a feed-forward plus PI feedback control.
The stability and stabilization of discrete-time linear positive switched systems are discussed in this paper. First, based on the concept of the forward mode-dependent average dwell time, a stability result for discrete-time linear positive switched systems is obtained by utilizing the multiple linear copositive Lyapunov functions. Then, by introducing multiple-sample Lyapunov-like functions variation, a new exponential stability result is derived. Finally, the conditions for the existence of mode-dependent stabilizing state feedback controllers are investigated, and two illustrative examples are given to show the correctness of the theoretical results obtained.
The intelligibility of an image can be influenced by the pseudo-Gibbs phenomenon, a small dynamic range, low-contrast, blurred edge and noise pollution that occurs in the process of image enhancement. A new remote sensing image enhancement method using mean filter and unsharp masking methods based on non-subsampled contourlet transform (NSCT) in the scope for greyscale images is proposed in this paper. First, the initial image is decomposed into the NSCT domain with a low-frequency sub-band and several high-frequency sub-bands. Secondly, linear transformation is adopted for the coefficients of the low-frequency sub-band. The mean filter is used for the coefficients of the first high-frequency sub-band. Then, all sub-bands were reconstructed into spatial domains using the inverse transformation of NSCT. Finally, unsharp masking was used to enhance the details of the reconstructed image. The experimental results show that the proposed method is superior to other methods in improving image definition, image contrast and enhancing image edges.
This paper studies the problem of designing a robust controller for the nonlinear multi-input multi-output continuous stirred tank reactor (CSTR) in the presence of uncertainties in parameters of the process model. For this purpose, by first using the feedback linearization method, the equivalent linearized model of the CSTR is obtained. In the second step, to cope with uncertainties, two robust controllers are designed; one using H mixed-sensitivity and the other using DK-iteration method. In this step, the required performance and uncertainties are expressed in terms of the suitable weight functions. Finally, the performance of the resulting feedback system is verified through numerical simulations.
To meet the more stringent environmental requirements of automobile exhaust gas emissions, diesel engines have recently received increased attention due to their high heat efficiency. To lower fuel consumption and reduce exhaust gas simultaneously, fuel combustion must be more precisely controlled. For example, the oxygen concentration, which affects emissions, is controlled by exhaust gas recirculation (EGR) and variable nozzle turbo (VNT). However, realizing a controlled design is difficult due to system non-linearity and strong interference between EGR and VNT. Recently, various design
methods have employed the so-called model-based control design, but this design approach is difficult to use when the controlled object is complex. Currently, mass production uses gain scheduling of map-based on proportional–integral–derivative (PID) control, in which each gain is tuned at various operational points. However, map calibration has many drawbacks, including time-consuming tuning, difficulty tuning during transient operations and problems adapting to the individual variations in the engine characteristics. This study proposes a construction method for a model-free adaptive PID controller using the simultaneous perturbation stochastic approximation (SPSA) and its performance is confirmed in an engine bench test.
The design of human assistive systems requires actuators that are capable of producing compliant motions. One key technological development for this compliant actuation is series elastic actuation, which is a challenging design problem, as the load side dynamics of the actuator changes during operation. In this paper, a novel series elastic actuator (SEA) structure using composite beams as series elastic elements and its torque control strategy are presented. We proposed a two-stage controller, a novel adaptive state feedback controller for transient dynamics and a PI controller for eliminating steady-state error. The effect of disturbance torque is studied, together with load dynamics, which is estimated online by a recursive least square method. The performance of the actuator was evaluated in case studies in which the actuator was controlled with various knee torque profiles. The experimental results showed that desired dynamic behaviour of the designed SEA can be obtained with the proposed control algorithm despite the unknown load dynamics.
Reliable and affordable compound sensors for measuring the water content and electrical conductivity simultaneously in soilless substrates are limited. In this study, a new compound sensor based on dielectric theory and four-electrode method is presented, and the size of the sensor is determined with the help of ANSYS software. A water content calibration test is designed in vinegar residue and coconut chaff with the same electrical conductivity. Test results show that the water content calibration equation is suitable for different substrates. Similarly, an electrical conductivity calibration equation is obtained in different concentrations of salt solutions. Considering the influence of electrical conductivity to water content measurement, a two-factor (water content and electrical conductivity) orthogonal test is designed in coconut chaff. Based on the analysis of test results, two compensation models – the multiplicative model and the additive model – are proposed. To evaluate the reliability of the models, a multiple linear regression analysis is employed. The results indicate that the additive model has a relatively higher reliability, i.e. the additive model can be regarded as the compensation model. After compensation, the relative errors of the measured values (including volumetric water content and electrical conductivity) are no more than 10%, and the compound sensor is suitable for different soilless substrates without recalibration.
Networked control systems (NCSs) are commonly modelled by the switched systems involving exponential non-linearities. A challenging problem is to obtain a tight linear approximation of the mentioned non-linear models to derive analysis and design criteria in terms of linear matrix inequalities (LMIs), which can be easily handled using the well-established algorithms. The present paper introduces a novel procedure to achieve an improved polytopic over-approximation of the non-linearities emerged in the modelling of NCSs. Moreover, stability of the NCSs is analysed to verify the merits of the proposed method; to this end, a benchmark numerical example is presented to illustrate the superior performance of the suggested approximation scheme compared with the existing approaches in the literature.
This paper presents a novel intelligent method based on local mean decomposition and multi-class reproducing wavelet support vector machines (RWSVMs), which are applied to detect leakage in natural gas pipelines. First, local mean decomposition is used to construct product function components to decompose the leakage signals. Then, we select the leakage signals which contain the most leakage information, according to the kurtosis features of these signals, through principal component analysis. Next, we reconstruct the principal product function components in order to acquire the envelope spectrum. Finally, we confirm the leak aperture by inputting envelope spectrum entropy features, as feature vectors, into the RWSVMs. Through analysing the pipeline leakage signals, the experiments show that this method can effectively identify different leak categories.
In this paper, finite-time synchronization for a class of Markovian jump complex networks (MJCNs) with generally uncertain transition rates (GUTRs) is considered. In this GUTR network model, each transition rate can be completely unknown or only its estimate value is known. This new uncertain model is more general than partly unknown transition rates (PUTRs). By constructing a suitable stochastic Lyapunov–Krasovskii function, using finite-time stability theorem and pinning control approaches, a sufficient finite-time synchronization criterion is derived in term of linear matrix inequalities (LMIs), which is easy to solve with the help of the LMI toolbox in Matlab. Finally, theoretical results are supported by numerical simulations.
A quadrotor is a kind of vertical takeoff and landing unmanned aerial rotorcraft which has been attracting wide interest around the world. However, small-scale quadrotors usually suffer from modeling error and external disturbance which are severe challenges for the control system design. To this end, the disturbance-observer-based (DOB) control strategy is applied to reject the performance degradation caused by the above negative factors. To further improve the trajectory tracking precision for an indoor quadrotor suffering from modeling error and external disturbance, a linear dual DOB (LDDOB) trajectory tracking control scheme is developed in this paper. Considering the practical implementation in engineering, the LDDOB position control approach is developed based on the linear quadrotor model. In addition, regarding to the non-minimum phase characteristic of the translational motion model, a modified version of a PID position controller is utilized to attenuate the undershoot of the closed-loop system in the initial response. Extensive numerical simulations show the effectiveness of the proposed trajectory tracking control scheme.
This paper proposes an effective fireworks algorithm (FWA), which is a new heuristic algorithm inspired by the phenomenon of a fireworks display, to solve the warehouse-scheduling problem. First, a real-world warehouse-scheduling problem is described in detail, and it is formulated as a constrained single-objective optimization problem. Then an effective FWA, FWA-LSCM, is developed by combining with a local search method and chaotic mutation, which are used to balance the exploration and exploitation of FWA. The experimental results show that FWA-LSCM is a competitive algorithm for a set of 2013 Congress on Evolutionary Computation (CEC) benchmark functions. Finally, the proposed FWA-LSCM is successfully applied to the warehouse-scheduling problem and outperforms other FWA algorithms studied in this paper.
In this paper, finite-time stability and boundedness for continuous positive switched systems with time-varying delay under state-dependent switching are investigated. By using a co-positive-type Lyapunov function method, sufficient conditions for solving the above-mentioned problems are given. It is proved that the sliding motion caused by the state-dependent switching will not destroy the performance of the system. The above results are extended to the discrete case. All the obtained results are formulated in the form of linear matrix inequalities (LMIs), which are computationally tractable. Finally, an illustrative example is given to verify the effectiveness of the proposed method.
A new composite guidance law for intercepting manoeuvring targets with desired terminal line-of-sight (LOS) angle constraint is proposed in this paper. The presented guidance law is derived through generalized model predictive control (GMPC) augmented by a target manoeuvre estimator. More specifically, the target manoeuvre estimator is used to estimate and compensate for the unknown target manoeuvre online while the GMPC is used to obtain optimal LOS angle tracking performance. Stability analysis shows that the LOS angle tracking error and the LOS angular rate can converge to a sufficiently small region around zero asymptotically. The effectiveness of the proposed guidance law is validated by applying it to a surface-to-air missile for intercepting a head-on manoeuvring target under different scenarios.
In this paper, fault detection and an isolation technique for an insulated-gate bipolar transistor open-circuit fault in a voltage source inverter are presented. This technique consists of analysing the pole voltage and providing the detection and the location of simple, simultaneous and multiple faults. Open-circuit faults can be detected by sensing the pole voltage of each leg and comparing it with the theoretical one. To improve the calculation speed and reliability of this technique and to avoid false diagnosis alarms, the fault detection and isolation scheme is based on a novel model of pole voltage taking into account the time delays due to the turn-on and turn-off process of the power switches. This method reduces the detection time and is applied for open-loop or closed-loop faults in a transient or steady state.
Robot-assisted surgery is being widely used as an effective approach to improve the performance of surgical procedures. Autonomous control of surgical robots is essential for tele-surgery with time delay and increased patient safety. In order to improve safety and reliability of the surgical procedure of tissue compression and heating, a control strategy for simultaneously automating the surgical task is presented in this paper. First, the electrosurgical procedure such as vessel closure that involves tissue compression and heating has been modelled with a multiple-input–multiple-output (MIMO) non-linear system for automation simultaneous. After linearizing the models, the linear-quadratic Gaussian (LQG) is used to control the tissue compression process and tissue heating process, and the particle swarm optimization (PSO) algorithm was used to choose the optimal weighting matrices for the LQG controllers according to the desired controlling accuracy. The LQG controllers with optimal weights were able to track both the tissue compression and temperature references in finite time horizon and with minimal error (tissue compression – the max absolute error was
This paper addresses the consensus protocol design problem for linear multi-agent systems with input saturation. Existing consensus protocols usually contain certain global information, such as network size or the spectrum of the Laplacian matrix, and this global knowledge is often unavailable to all agents. In this paper, based on only the agent dynamics and the relative states of neighbouring agents, a novel adaptive consensus protocol is designed by assigning a time-varying coupling weight to each node. This protocol has two advantages: it is independent of any global information, and hence is fully distributed; and it is implemented by actuators with input saturation constraints. By combining the low-gain feedback method and appropriate Lyapunov techniques, it is shown that our protocol can achieve the semi-global consensus tracking in a fully distributed fashion, under the mild assumptions on agent dynamics and the topology graph. The results are illustrated by numerical simulations
This paper investigates the problem of disturbance attenuation by output feedback for a class of minimum-phase nonlinear uncertain systems with unknown control direction. In order to deal with the difficulties brought by the unknown direction, we first transform the original system into a new form by a linear state transformation. Then, based on a state observer, a constructive output feedback design procedure is proposed to solve the disturbance attenuation problem in the sense of L2-gain. The efficiency of the control method is demonstrated by a simulation example.
A number of referenced materials in the area of real-time tracking have been considered to present a novel intelligent estimation framework (IBEF). The main idea behind the research is to realize an approach that is applicable and efficient with respect to the final outcomes in the same way. The IBEF is realized, as a decision maker, in association with the neural network, where all the types of chosen objects, such as military, private vehicles, animals and other related objects, may be tracked in a set of frames of video through their approach. Then, the IBEF in each frame should be corrected to re-organize the estimation, in a constructive manner. The main goals may be chosen as solid, non-solid, stationary and non-stationary objects, as long as all of them can be taken in different quantities. The applicability of the approach can be presented in the areas of flight control, collision avoidance, etc. The process of realizing the approach can be continued by extracting some prominent features regarding the tracked objects, in order to apply them to the IBEF. The desirable performance of the proposed approach is guaranteed though a standard scenario. The results are finally compared with a benchmark approach, where the improvement of the approach performance can be verified.
The consensus problem of heterogeneous first-order multi-agent systems with diverse nominal velocities is investigated in this paper, and three adaptive consensus algorithms are constructed by introducing an adaptive variable into the usual consensus algorithm. Under fixed interconnection topology, consensus criteria are obtained for the three algorithms. Moreover, delay-dependent consensus criteria are also presented for the three algorithms in synchronously coupled form under identical input delay and communication delay. Numerical examples are presented to illustrate the validity of our theoretical results.
In this paper, the problem of chaos control of the parameter-uncertain permanent magnet synchronous motor (PMSM), with both fixed and uncertain pulse disturbance, is discussed. In the operation of the motor, disturbance is widespread which leads to the emergence of chaotic behaviours, which threatens the security of the drive system. To control the undesirable chaos in the PMSM, a new control scheme which depends on the external torque is designed. Based on the Lyapunov stability theorem and matrix theory, several sufficient conditions have been derived to ensure the global asymptotical stability and the exponential stability for the pulse disturbed and parameter-uncertain PMSM. A numerical example is given to demonstrate the effectiveness of the proposed results.
This paper studies the global asymptotic stabilization of a class of upper-triangular systems without a priori knowledge of control directions under the homogeneous domination. The homogeneous domination is used in this paper to design state feedback controllers. The Nussbaum-type gain method is also introduced to complete the design of the controller. The controller is not only used to ensure the global boundedness for all signals of the closed-loop system, but also to guarantee that the system state asymptotically converges to zero. Finally, the simulation results are given to illustrate the effectiveness of the proposed approach.
A measurement system based on a W-type optical fibre sensor for determining the fat content in milk is investigated in this paper. The system consists of a light source, W-type optical fibre sensor, detector, amplifier, A/D converter, microprocessor and thermoelectric cooler (TEC). According to Mie-scattering theory, the standard models of the system at different temperatures (25°, 30°, 35°, 40° and 45 °C) are obtained. Evaluations of all the models at different temperatures are made, which illustrate that 40 °C is the optimal temperature for fat content in milk. At 40 °C, the linear relationship between the absorbance and the fat content is significant. Furthermore, a prediction experiment has been performed to confirm the validity of the standard model. As a result, the measurement system based on a W-type optical fibre sensor is capable of measuring the fat content in milk effectively and in real time.
The main focus of this paper is on a graphical tuning method of non-linear fractional-order PID (FOPID)-type controllers, i.e. a class of FOPID-type controllers that non-linearly depend on the control parameters, e.g. FO[PI], FO[PD] etc. Firstly, a method is proposed to determine the stabilizing region of non-linear FOPID-type controllers, namely the complete sets of FOPID-type controllers providing stability of the control system. Secondly, two different approaches are proposed to determine the H region of these FOPID-type controllers, namely the complete sets achieving H robust performance specifications. The first approach maps the H constraints into the parameter space by solving a series of non-linear equations. The second approach transforms the original H region problem into simultaneous stabilization of a family of characteristic polynomials. It turns out that these two approaches are both very flexible, and the second approach is more efficient than the former. The main advantage of our proposed graphical tuning method is that the exact mathematical model of the controlled plant is not needed. The stabilizing and H regions can be computed only from the frequency response data of the plant. Finally, numerical and experimental results are presented to demonstrate the proposed graphical tuning method.
In this paper, a robust nonlinear control design using an operator-based robust right coprime factorization approach is considered for vibration control on an aircraft vertical tail with piezoelectric elements. First, a model of the aircraft vertical tail is derived to describe vibration response using the operator-based approach, where, to stabilize vibration of the tail, piezoelectric elements are used as actuators and a hysteresis nonlinear property of piezoelectric actuators is considered. Simultaneously, positions of the piezoelectric actuators that are stuck on the plate are arranged by using a finite element method. Then based on the obtained operator-based model, a robust nonlinear feedback control design is given by using robust right coprime factorization for the aircraft vertical tail with considering the effect of hysteresis nonlinearity from piezoelectric actuators. In particular, low-order modes are employed to design the control scheme even though vibration is configured by high-order modes. In other words, robustness is considered, and the desired performance of tracking is discussed. Finally, both simulation and experimental results are shown to verify the effectiveness of the proposed control scheme.
In this paper we consider finite-time consensus control for a group of second-order systems, including double-integrator systems and nonlinear mechanical systems. Distributed controllers are designed such that a consensus is reached while all the agents in the multi-agent system suffer from input saturation. Firstly, we propose distributed controllers for multiple double-integrator systems under the leader–follower scenario while only part of the agents have access to the leader. We also assume that the velocities of the agents are not measurable, thus an observer is designed for each of the agents. Secondly, the methodology is extended to the leader–follower consensus control for a group of nonlinear mechanical systems. It is shown that the states of the mechanical systems can reach a consensus within finite time under input saturation. Finally, simulation results illustrate and verify the effectiveness of the proposed schemes.
In this paper, an embedded control system is developed to measure the yield strength of the material plate with an applied load. A systematic approach is proposed to handle special requirements of embedded control systems, which are different from computer-based control systems, as there are much less hardware resources and computational power available. An efficient control algorithm has to be designed to remove the CPU burden so that the microcontroller has enough power available. A three-step approach is proposed to address the embedded control issue: firstly, the mathematical description of the whole system is studied using both theoretical and experimental methods. A mathematical model is derived from the physical models of each component used, and an experiment is retrieved by employing Levy’s method and least-square estimation to identify specific parameters of the system model. Secondly, a feedforward plus feedback controller is designed and simulated as a preparation for the embedded system implementation. The cerebellar model articulation controller (CMAC) is chosen as the feedforward part, and a PD controller is used as the feedback part to train the CMAC. Finally, the proposed algorithm is applied in the embedded system, and experiments are conducted to verify both the identified model and designed controller.
This note presents an iterative algorithm to solve the coupled Sylvester-transpose matrix equations (including the generalized coupled Sylvester matrix equations and Lyapunov matrix equations as special cases) over generalized centro-symmetric matrices. When the considered matrix equations are consistent, for any initial generalized centro-symmetric matrix group, a generalized centro-symmetric solution group can be obtained within finite iteration steps in the absence of roundoff errors. The least Frobenius norm generalized centro-symmetric solution group of the coupled Sylvester-transpose matrix equations can be derived when a suitable initial generalized centro-symmetric matrix group is chosen. In addition, for a given generalized centro-symmetric matrix group, the optimal approximation generalized centro-symmetric solution group can be obtained by finding the least Frobenius norm generalized centro-symmetric solution group of new coupled Sylvester-transpose matrix equations. Finally, a numerical example is given to demonstrate the efficiency of the introduced iterative algorithm.
Structure determination and parameter identification of multivariate systems are crucial but rather difficult issues in system identification. Due to the explosive growth of process data along with the scale increase of industrial processes, directional links between variables of such complex processes are often undistinguishable, which is indispensable to model structure determination but is often assumed to be known beforehand in most identification methods. In this article, a new modelling approach is developed to simultaneously estimate the model parameters and structures (including model orders as well as the directional links between different process variables) of multivariate systems. A vector auto-regressive (VAR) form is utilized as the model formulation in this algorithm. The key technique lies in constructing an interleaved information matrix with respect to a multiple model structure formulated for the VAR representation. Then by utilizing the upper diagonal factorization, all the parameter estimates of all path models with orders from zero to m, as well as the corresponding cost function values, can be obtained simultaneously. The effectiveness of the proposed method is demonstrated via a numerical example and a distillation column system.
In this paper, the leader-following consensus problem of discrete-time descriptor multi-agent systems is considered. Each agent’s dynamics is modelled by a discrete-time linear descriptor system, and the interaction topology among the agents is assumed to be directed. To solve the multi-agent consensus problem, three different architectures of connecting observers and controllers are used to construct the consensus protocols. A kind of modified generalized algebraic Riccati equation is provided to design the protocol’s gain matrices. Based on graph theory, matrix theory and the Lyapunov method, some sufficient convergence conditions are established to guarantee the descriptor multi-agent system achieves consensus. Finally, a simulation example is provided to illustrate the obtained results.
This research attempts to develop a control approach for stabilizing a platform mounted on a base plate whose orientation can be arbitrarily modified. The end goal is to stabilize a camera for earth observation purposes. MEMS (micro-electromechanical system)-based gyroscopes and accelerometers are utilized as inertia references and are placed on the camera platform. The undesired drift of MEMS gyro data is eliminated by data confusion using a complementary filter. To design a stabilizing controller, first a mathematical model for the platform is developed and system uncertainties raised from the unmeasured states are shown to be bounded. Then this fact is utilized to propose an integral-based sliding mode control strategy to stabilize the platform in the inertia coordinate system under a wide variety of external angular rates and accelerations. Finally, the encouraging control performance is demonstrated experimentally.
A neural network Hamilton–Jacobi–Bellman (HJB) approach is introduced to deal with the spacecraft rendezvous problem with target spacecraft in arbitrary elliptical orbit. The Lawden equations are utilized to describe the relative motion of two spacecrafts. A generalized non-quadratic functional is introduced to describe constrained control. An approximate solution to the value function of the HJB equation corresponding to constrained controls is obtained by solving for a sequence of cost functions satisfying a sequence of Lyapunov equations. An inverse optimal controller is introduced to design the initial stabilizing admissible control for successive approximation. Furthermore, an optimal control law is obtained to stabilize the closed-loop system under constrained controls, and the spacecraft rendezvous mission can be accomplished with the nearly optimal controller. In comparison with the existing quadratic-regulation-based approaches used to deal with the rendezvous problem, which requires the value function of the nonlinear differential equations, the optimization factor and constrained control are taken into consideration simultaneously, and an approximate optimal constrained state feedback controller has been tuned a priori off-line. Stability analysis as well as simulation results are provided to illustrate the effectiveness of the presented approach.
In this paper, we present a comparative study of four voting algorithms in two observer-based fault-tolerant control (FTC) architectures for an electric vehicle (EV) induction motor drive. The first architecture, called output FTC, is based on the mechanical sensor, an EKF and a second-order sliding mode observer (SMO2). The second one, input FTC, is based on three controllers (PI, H loop shaping and the generalized internal model control), the most appropriate being selected to ensure good behaviour in presence of a multiplicative sensor fault (the fault is modelled as an exponential type emulating a bias). A third architecture, called hybrid FTC, based on the previous output and input fault-tolerant schemes, is built to mitigate simultaneous faults. Simulation and experimental results for a 7.5-kW induction motor drive show the efficiency of the approaches and their robustness against parametric variations for different load conditions.
The Multiple-model Adaptive Estimation method has low capability to track abrupt faults; therefore, multiple fading factors may result in diverging the Strong Tracking Filter. Moreover, the fault probability calculation is large. In this paper, an improved Strong Tracking Multiple-model Adaptive Estimation fast diagnosis algorithm is proposed. The tracking performance of the filter was improved by multiple fading factors. An improved renewal equation of the step prediction covariance matrix is proposed. The stability of the filter was guaranteed, and the estimation accuracy was improved. Based on the Euclidean norm, a fast fault isolation method that reduces the fault probability calculation is proposed. The simulation results show that this algorithm is more efficient and has a better performance.
This paper presents an optical flow-based novel technique to perceive the instant motion velocity of a smart wheelchair robot. The primary focus of this study is to determine the wheelchair’s ego-motion using a displacement field in temporally consecutive image pairs. In contrast to most previous approaches for estimating velocity, the proposed strategy has two main innovations. Firstly, the proposed tilted overlooking camera set-up instead of conventional downward-looking camera and the corresponding ego-motion model is presented for compact indoor mobile robots. Secondly, by virtue of the graphic processing unit-accelerated TV-L1 algorithm, which is coupled with motion priors-based pixel prediction, we are permitted to improve the accuracy and efficiency of the optical flow estimation significantly. In order to render our method more robust with respect to noise and outliers, we propose a quadratic motion model-based random sample consensus (RANSAC) refinement of flow fields. Advantages of our proposal are validated by real experimental results carried on our smart wheelchair platform and contrast evaluations conducted on Pioneer robot.
An adaptive extended Kalman filter is designed to estimate the arc length in a gas metal arc welding system. The simulation results show that the estimated variables track the true variables of the non-linear model with negligible error and are robust against parameters uncertainties. The proposed estimator also operates adequately in a highly noisy welding environment. Because of the low computational requirements and little lag produced in the process dynamic, use of the proposed estimator would be valuable in the design of a controller for the gas metal arc welding system.
This paper presents a particle filter design to improve the accuracy of received signal strength indicator (RSSI)-based localization algorithms for localizing mobile robots that move in an environment with an 802.15.4 ZigBee wireless sensor network. In this study, the RSSI of transmission signals is adopted to estimate the location of a mobile robot by applying the trilateration algorithm. However, the interference and reflection effects of transmission signals and the orientation effects of the antenna can significantly affect the RSSI signals and result in serious localization errors. This study analyses the characteristics of RSSI signals and the orientation effects of the antenna and then develops a particle filter design with the fusion of the current states of the mobile robot to improve localization accuracy. A static localization experiment shows that the developed localization algorithm can significantly improve the localization accuracy, and a maximum rate of improvement of up to 85% is achieved in the test environment. A dynamic localization experiment demonstrates that the developed algorithm is suitable for localizing a mobile robot.
In this study, the problem of fractional-order (FO) output feedback controller design for FO Takagi–Sugeno (TS) fuzzy systems with deterministic parameters and unmeasurable premise variables has been investigated, and the FO is in the range of 0 to 2. First, the FO TS fuzzy system is changed to an equivalent FO system with uncertain parameters. Then, a FO output feedback controller for the equivalent FO uncertain parameter system can be designed. In terms of linear matrix inequality, an explicit expression for the designed FO output feedback controller is found. Consequently, the FO TS fuzzy system is shown to be stabilized by the designed FO output feedback controller. Examples are included to demonstrate the effectiveness of the proposed method.
A position and orientation systems (POS) plays an important role in aerial mapping applications. It integrates the inertial navigation system and global positioning system to provide high-precision position, velocity and attitude for various aerial mapping sensors. However, in severe environment of temperature, magnetic field and vibration in the application of aerial mapping, the precision of gyroscopes and accelerometers may degrade. The traditional Kalman filter may perform poorly when the model of gyroscope and accelerometer errors is uncertain. This paper highlights the use of multiple fading factors for a strong tracking Kalman filter (STKF) to accommodate the model uncertainty of gyroscope and accelerometer errors. Through utilizing the information of the sensitivity matrix of a two-stage Kalman filter, the multiple fading factors are obtained adaptively. Therefore, a more accurate covariance matrix is obtained in the proposed algorithm, and a better state tracking ability is achieved than with the Kalman filter and the STKF. Finally, a flight experiment is demonstrated to validate the effectiveness of the proposed algorithm. It is shown from the experimental results that the proposed algorithm can more accurately estimate the time-varying errors of gyroscopes and accelerometers than Kalman filters or the STKF; the accuracy of position, velocity and attitude of POS is also improved correspondingly.
There is an issue with the timely application of artificial intelligence to humankind, particularly to the disabled. In this paper, we propose a surface electromyography signal recognition system for prosthetic application to persons with disabled hands, which achieves simplicity, reliability, accuracy and a short response time by increasing the performance of each part of the system and the coordination between the interconnected components. First, a surface electromyography signal acquisition system was designed on the basis of the cost and processing speed. Second, a method called the ‘extreme value’ was carried out on the original signal containing five continuous movements, by separating the signal into isolated segments representing different postures, which made application of the system for daily use possible. Third, on the basis of time-domain, chaos-theory and time–frequency-domain analysis methods, four features, namely the average amplitude, fractal dimension, maximum Lyapunov exponent and wavelet coefficient, were extracted from four possible arm locations to be determined. Furthermore, the average amplitude from the extensor digitorum and the wavelet coefficient from the flexor pollicis longus were determined as the final features after comparing the clustering effects of the extracted features. Finally, a new strategy for classifying the different postures based on a back-propagation neural network was introduced to obtain an average system accuracy of 82.77% for five continuous movements.
Monitors are added to problematic siphons to avoid deadlocks. Li and Zhou (2004, 2006a,b, 2008a,b,c) add monitors to elementary siphons only while controlling the rest-dependent siphons to save on costs. After failing a marking linear inequality (MLI) test, Li and Zhou perform a linear integer programming (LIP) test (NP-hard). We proposed a new MLI test earlier to avoid the LIP and extended it to systems of simple sequential processes with general resource requirements (S3PGR2) for strongly dependent siphons (SDSs). The control policy for weakly dependent siphons (WDSs) is rather conservative due to some negative terms in the MLI. This paper shows that WDS and SDS have the same controllability (i.e. MLI). As a result, the control for WDS need no longer be that conservative. We also develop an optimization (by redundancy elimination) of the computation required for the LIP test to ensure deadlock prevention of systems of simple sequential processes with resources (S3PR). A favourable result for this policy is that any n-dependent (n > 2) WDS (similar to SDS) needs no monitor and hence the complexity for synthesizing a controller becomes polynomial. Application to a slight variant of a well-known benchmark is illustrated.
This research work proposes a method to obtain a stable reduced-order interval model from its stable higher-order interval plant. The reduced-order interval numerator and denominator polynomials are determined by using Kharitonov’s polynomials and a general form of the Routh approximation method. The proposed reduction algorithm retains stability and full impulse response energy of the higher-order interval system in its reduced-order interval model. In addition to this, the proposed method has useful features like matching of time moments and mathematical simplicity. Moreover, a few numerical examples in the literature are taken into consideration and simulated through MATLAB to illustrate the effectiveness of the proposed method.
Much of the research which has been done on special kinds of matrix equations has almost the same structure. In this paper, an iterative approach is proposed which is more general and includes all such matrix equations. This approach is based on the global least squares (GL-LSQR) method. To do so, a linear matrix operator, its adjoint and a proper inner product are defined. Then we demonstrate how to employ the new approach for solving the general linear matrix equations system. When the matrix equations are consistent, a least-norm solution can be obtained. Moreover, the optimal approximate solution to a given group of matrices can be derived. Also, some theoretical properties and the error analysis of the new method are discussed. Finally, some numerical experiments are given to compare the new iterative method with some existent methods in terms of their numerical results and illustrate the efficiency of the new method.
Process monitoring is essential and plays a vital role in enhancing the quality of the output. For this purpose, many statistical tools are used in practice and the control chart is one of the most popular choices. Bayesian and classical set-ups are two major and popular categories for defining the design structures of different types of control charts. This study planned to investigate the Bayesian control charts under different loss functions to ensure an efficient monitoring of process parameters for quality control. The performance measures used in this study are average run length (ARL), relative ARL (RARL), extra quadratic loss (EQL) and performance comparison index (PCI). It has been observed that the application of Bayesian structure of process monitoring needs careful consideration in terms of prior distribution, sampling and posterior (predictive) distributions, and the choice of loss functions, in order to obtain reliable outcomes.
The problem of error estimation and compensation in strapdown inertial navigation system (SINS) is investigated in this paper. The error dynamic model is derived and employed for this purpose. Information gathered from a micro-electro-mechanical inertial measurement unit (MEMS IMU) is fused with camera information in a loosely coupled integration scenario. Although this integration is computationally efficient, it suffers from fault propagation in the navigation algorithm. To overcome this problem, it is proposed in this paper to estimate and compensate for the propagated faults in a timely manner to provide a short start-up and computation time. This goal is achieved through the design of decentralized error estimation and compensation algorithms running in parallel with the inertial navigation system. To this end, firstly, the sparse error system is decomposed to cascaded subsystems using a combination of structural and behavioural decomposition methods. Then, cascaded Kalman filters (KFs) and decentralized state feedback regulators are designed for error estimation and compensation, respectively. The experimental results based on data from the 3D MEMS IMU and camera system are provided to demonstrate the efficiency of the proposed method.
Reinforcement learning was developed to solve complex learning control problems, where only a minimal amount of a priori knowledge exists about the system dynamics. It has also been used as a model of cognitive learning in humans and applied to systems, such as pole balancing and humanoid robots, to study embodied cognition. However, closed-form analysis of the value function learning based on a higher-order unstable test problem dynamics has been rarely considered. In this paper, firstly, a second-order, unstable balance test problem is used to investigate issues associated with the value function parameter convergence and rate of convergence. In particular, the convergence of the minimum time value function is analysed, where the minimum time optimal control policy is assumed known. It is shown that the temporal difference error introduces a null space associated with the experiment termination basis function during the simulation. As this effect occurs due to termination or any kind of switching in control signal, this null space appears in temporal differences (TD) error for more general higher-order systems. Secondly, the rate of parameter convergence is analysed and it is shown that residual gradient algorithm converges faster than TD(0) for this particular test problem. Thirdly, impact of the finite horizon on both the value function and control policy learning has been analysed in case of unknown control policy and added random exploration noise.
This paper presents an analytical–synthetic model of ergonomic research of the abilities of the dispatcher/operator in the control room for the automatic control of railway transportation. Twenty performance indicators describing the operators’ performance (work capacity and perceptive abilities) and the main characteristics of the control room (work organization and ergo-technical analysis) are identified. A group fuzzy analytic hierarchy process is applied in the process of ranking and selection of key performance indicators of railway control rooms. The selected key performance indicators (the operator’s hand movement, visual symptoms of fatigue, device error analysis, location and dimensions of the control desk) are analysed in detail.
This paper investigates the problem of robust and non-fragile
This paper presents a central pattern generator (CPG)-based locomotion controller, with a foot trajectory generator and sensor-driven reflex, aiming to increase the adaptability of the hexapod robot walking on uneven terrain. The trajectory generator was used to precisely control the foot trajectories to constrain the leg movement in the workspace. The foot trajectories were further shaped by the sensor information from the proposed sensor-driven reflex, making the hexapod able to pass obstacles of different sizes on uneven terrain. In addition, a novel neck joint was designed to implement the sensor-driven reflex, with an active control mechanism for the climbing of high steps or large obstacles. Finally, a series of simulations and prototype tests were carried out to prove the feasibility of the proposed controller. The results showed that with the integration of the foot trajectory generator, sensor-driven reflex and active neck joint, the hexapod robot can achieve a stronger adaptability to uneven terrain and better performance in walking.
This paper addresses the network-based leader–following consensus problem for the second-order multi-agent systems with nonlinear dynamics. Based on the Lyapunov–Krasovskii theory, a new delay-dependent sufficient condition in terms of linear matrix inequalities (LMIs) is presented to guarantee the consensus of the multi-agent system, and a sufficient condition for network-based controller design is proposed to ensure the followers reach consensus with the leader for second-order multi-agent systems with nonlinear dynamics. The effectiveness and applicability of the suggested solution is evaluated and verified through the simulation of two numerical examples.
In this paper, the robust non-fragile stabilisation and H control problem is investigated for a class of uncertain discrete-time stochastic systems with Markovian jumping parameters and time-varying delay. The parameter uncertainties are supposed to be time-varying as well as norm-bounded. The aim of the robust non-fragile stabilisation problem is to design a non-fragile state feedback controller which guarantees the robust stability of the closed-loop system for all admissible uncertainties. At the same time, in addition to the robust stability requirement, a prescribed H performance level is required to be achieved for the robust H control problem. By Lyapunov stability theory, delay-segment-dependent conditions for the solvability of these problems are formulated in terms of linear matrix inequality technique. Finally, numerical examples are shown to demonstrate the usefulness and applicability of the proposed design method.
In this paper, in order to synthesize a control law we propose a new approach that enables identification of the intermediate equilibrium points of a nonlinear system, knowing the first and the last ones. These points are those around which the nonlinear system is linearized and therefore yields local models (sub-models) that contribute to forming the multimodel describing the nonlinear system. This approach is based on the transition from a given point (source) to the next by varying a scheduling parameter (SP) defining the source point sub-model. The variation of this parameter is limited by the maximum value of the stability margin determined by the loop shaping design procedure approach (LSDP) applied to such a sub-model. Hence, the new equilibrium point is defined by the new obtained value of the SP for which the gap metric between this sub-model and the one corresponding to the new value of SP is larger than the given stability margin. The different robust controllers synthesized for the different equilibrium points will be used to synthesize the robust control of the nonlinear system, by applying the gain-scheduling technique. The proposed transition approach as well as the robust control algorithm were validated on the continuous stirred tank reactor (CSTR) system.
In this paper, the interference with magnetic heading estimation caused by a local near-surface magnetic anomaly is studied and an error compensation method based on discrete cosine transform (DCT) is proposed. Firstly, the magnetic gradient signals of the magnetic anomaly source are calculated from the measured vector sum of the anomalous magnetic field and the background geomagnetic field. Then the magnetic vector signals generated by the near-surface magnetic anomaly sources are calculated by the time-domain differential theorem of DCT. Finally, the background geomagnetic field vector is obtained and compensation for the magnetic heading perturbation is achieved. Numerical simulations show that the proposed method can accurately compensate for the magnetic heading perturbation caused by near-surface magnetic anomaly sources, and experimental results reveal the reliability and practicability of the proposed method.
Direct-driven spindles have no mechanical transmission trains and gears, and are the key actuators for computerized numerical control machine tools. These magnetically suspended rotor systems are required to provide fast response and high precision. However, these systems are non-linear and strongly coupled. The traditional proportional, integral, derivative (PID) control method has been widely used for such systems owing to its relative simple realization. However, the tracking, disturbance rejection and robustness properties of the controlled plant may not be satisfied. To solve these problems, this paper presents a decoupling control strategy based on an inverse system scheme and combines it with the internal model control method to guarantee system robustness to the parameter uncertainty and external disturbance. By introducing an inversion of the magnetically suspended rotor system into the original system, a new pseudolinear system is developed. It can be shown that this addition effectively eliminates the influence of the unmodelled dynamics, and improve the accuracy and robustness of the whole system. The simulation and experimental results show that when compared with the traditional control scheme, the proposed control scheme provides good decoupling and robustness performance for the magnetically suspended rotor system in different operating conditions.
In reset control systems, the reset law determines reset values at reset times. This paper considers the observer-based reset law design for uncertain systems with both nonlinear uncertainties and time-varying parameter uncertainties. A model predictive strategy is proposed to design the reset law by minimizing a quadratic performance index. The optimization problem is transformed into a linear matrix inequalities (LMIs) formulation, and the reset law is solved by using an LMI technique. The proposed approach is applied to a typical continuous stirred-tank reactor system to show the effectiveness of the obtained results.
With the development of power system interconnection, low-frequency oscillation is becoming more and more prominent, and may cause instability of power systems. In this paper, two sources of uncertainty are modelled: plant and controller uncertainties. The non-linear dynamics of power systems under wide load variations are represented by a linear model with uncertainty in the form of a norm-bounded structure. The controller uncertainty resulting from resistor tolerance used in practical implementation is also represented by a norm-bounded model. This paper presents the design of resilient power system stabilizers (PSSs). The proposed PSS keeps stability against plant uncertainty and controller gain errors in the form of a linear matrix inequality (LMI)-sufficient condition. In addition to robust stability, the PSS design achieves regional pole placement to control the settling time and damping ratio, and consequently achieves good dynamic performance. The simulation shows that the proposed PSS design is efficient using the power system model and can drastically improve the dynamic performance of a single machine and a multi-machine power system.
State estimation and dynamical model identification from the observed data have attracted much research effort during recent years. In this paper, an identification method of a system based on the unscented Kalman filter (UKF) and group method of data handing (GMDH)-type neural network is introduced and applied. Probabilistic metrics, instead of deterministic metrics, are used to obtain a robust Pareto multi-objective optimum design of the UKF-based GMDH-type neural network. The simulation results show that the UKF-based training algorithm performs well in modelling some explosive cutting and forming processes, and exhibited more robustness in comparison with those using a traditional GMDH-type neural network.
Control charts are widely used to monitor manufacturing processes for a deterioration in stability. We propose repetitive group sampling control charts based on the process capability index C pk for monitoring process average when the quality characteristic follows a normal distribution. The performance of the repetitive C pk control chart is reported and compared with the existing control chart in terms of the average run length. It is found that the proposed control chart is effective for quickly detecting small shifts in the process mean.
This paper is concerned with optimal trajectory control for two unmanned aerial vehicles (UAVs) in a multisource localization environment. The received signal strength (RSS) at the UAVs in specified time intervals permits passive differential RSS (DRSS)-based localization of multiple radio frequency (RF) sources with unknown transmit powers. A steering algorithm is proposed to update the UAV waypoints in order to minimize the summation of the uncertainty of the source locations. The UAV paths are optimized by maximizing the determinant of the Fisher Information Matrix (FIM). The FIM is approximated at successive waypoints using the estimated locations of the sources. In addition to maximizing the localization accuracy, the objectives of the proposed trajectory control are to minimize the number of UAVs, the mission time and the path length. As the DRSS is a non-linear measurement, an extended Kalman filter (EKF), which is a non-linear filtering technique, is considered in this paper. The efficiency of the approach is depicted through simulations.
In the unmanned aerial vehicle (UAV) based localization of slow-moving radio frequency (RF) sources with unknown transmitted strength of signal, such as a person with a cell phone in a search and rescue mission, the UAV navigation errors are significant sources of localization error. Although the use of a global positioning system (GPS) can reduce the UAV’s localization error significantly resulting in more accurate RF source localization, if the GPS signal is lost temporarily or permanently, the accuracy of the UAV-based localization decreases rapidly. In this paper, a simultaneous localization and mapping (SLAM)-inspired approach for simultaneous localization of UAV and RF sources (SLUS) is proposed. The proposed approach solves these two connected problems, i.e. the UAV localization and RF source localization, simultaneously to decrease the error of the UAV position estimation and the error of the RF source localization. In the proposed approach, beside the UAV position prediction, the RF source position prediction is also performed. Then the predicted states are augmented and the augmented predicted state information is corrected using range-ratio and bearing observations, i.e. RF source features, considering the unknown transmitted power. The proposed approach is simulated and the results show that the normal divergence of a target localization and the divergence of the UAV navigation in latitude and longitude channels have been eliminated using this approach. In other words, simultaneous localization of the UAV and RF sources uses the RF sources, as features in the environment, to aid the navigation system. Although the approach is similar to the mapping of RF sources in the environment, the created map would be useless after finding the RF sources. That is why SLUS has been used instead of SLAM. The main contributions of this work are: 1) performing simultaneous localization of a UAV and targets using RF signals, especially in a non-line of sight (NLOS) condition; 2) using difference of signal strength, i.e. differential received strength signal indicator (DRSSI), to eliminate the impact of unknown target signal power; and 3) simultaneous multi-target localization and tracking.
In this paper, the mean square error (MSE) of the new technique, the shifted scaled least squares (SSLS), and the minimum MSE (MMSE) estimator is analysed in the Rician distributed flat fading multiple-input–multiple-output (MIMO) channels. First, the closed form expressions are obtained for MSE of the estimators using the estimated and the actual mathematical expectation matrix of the channel and the matrix of channel covariances. It is analytically and numerically shown that the performance of the estimators is less sensitive to the erroneous estimation of the Rice factor at the receiver. On the other hand, it is shown that the performance of the MMSE estimator is quite sensitive to the erroneous estimation of the channel correlation coefficient. In order to estimate the channel Rice factor, two algorithms are also proposed in this paper. These algorithms work based on the optimal training signal and least squares (LS) technique. Finally, the estimated Rice factor is used in the SSLS and MMSE estimators. Simulation results confirm the efficiency of the algorithms and the robustness of the above-mentioned estimators to the erroneous estimation of Rice factor.
In this paper, we propose a novel control methodology based on zero-dynamics theory for a class of wheeled inverted pendulum (WIP) vehicles, which is efficient even in the presence of uncertain system frictions and dynamics parameters. The control schemes are elegantly constructed so that the WIP vehicle can successfully implement stabilizing of the posture (longitudinal and rotational movements), as well as hold the upright position of the vehicle body (tilt angle stability), only by the two control inputs with the aid of the design approach of zero-dynamics. In particular, the dynamics uncertainties, especially the friction effects, would deteriorate the control performance severely in practice. Therefore, we employ adaptive laws for the design parameters of zero-dynamics subsystem and uncertain coefficients of parametric frictions and dynamics. Consequently, the estimated frictions and dynamics are compensated through feedforward to obtain better control performance. To enhance the robustness of the system against parameter variations and external disturbances, sliding mode control techniques are applied to derive the specific algorithms, and then the closed-loop systems are proven to be globally asymptotically stable by Lyapunov techniques and LaSalle’s invariance theorem. In addition, simulation studies have been performed to demonstrate the feasibility and effectiveness of the proposed strategies, which illuminate the promising practical application potentiality of the designed WIP vehicle control system.
A novel design method of decoupling internal model control is proposed for non-square processes with multiple time delays that are often encountered in complicated industrial processes. The method can obtain a realizable decoupling controller of non-square processes with more inputs than outputs by inserting some compensated terms, which are derived analytically. Meanwhile, based on the relative normalized gain array, an equivalent transfer function matrix is introduced to approximate the pseudo-inverse of the process transfer function matrix, which makes the design of decoupling internal model control simple and easy to calculate. Filters are added to the control structure to improve the robustness. Simulation results have proved the effectiveness and reliability of the proposed method.
This paper deals with the practical aspects of the implementation of the balance-based adaptive control (B-BAC) technique. It gives the simple mathematical background of this methodology, shows the case-independent implementation concept as the ready-to-use flexible function block that results from the B-BAC generality, discusses how to embed the additional functionalities required in professional industrial control applications and finally presents three implementation examples for three different programming and hardware platforms. The control performance of each B-BAC function block is validated experimentally and compared with the performance of the corresponding platform-related standard PID function blocks. The comparative results and the potential accessibility of both the flexible function block and the simple tuning method put forward B-BAC methodology as a strong alternative for PID-based industrial control applications.
In this paper defines an adaptive hybrid complex projective synchronization method to synchronize two chaotic complex systems. Base on Lyapunov stability theory, the adaptive control law and parameter update law are derived to make the state of two chaotic complex systems, which are adaptive hybrid complex projective synchronized. This paper extends projective synchronization from the field of real numbers to the field of complex numbers for a chaotic complex system. Different components of the complex system synchronized to different scale complex numbers. Numerical simulations are presented to demonstrate the effectiveness of the proposed adaptive controllers.
U-tube steam generator level control systems are used to maintain the water level within prescribed narrow limits and to provide constant supply of high-quality steam during power demand variations. Traditional level control systems are often found to be unsatisfactory during low power operations and start-up conditions. Robust non-linear estimator-based optimal control systems are proposed for steam generator level control systems to solve the water level tracking problem during power (or steam) demand variations. It is shown that the proposed control strategies provide optimal and robust water level tracking with a single controller over the complete range of power operation with model and parameter uncertainties and noisy measurements.
Wireless sensor networks have been utilized to monitor complex manufacturing processes but missing data from sensors cause problems for data-based applications. In this paper, a missing data estimation algorithm, GS-MPSO-WKNN (Gaussian mutation and simulated annealing-based memetic particle swarm optimization for weighted K nearest neighbours), based on a weighted K nearest neighbour (WKNN) and memetic computing is proposed. The GS-MPSO developed in our previous work is adopted in order to adjust the feature weights for the WKNN. A real world data set from a semiconductor manufacturing process is used to evaluate GS-MPSO-WKNN. Experimental results show that GS-MPSO-WKNN can reach a higher estimation accuracy, and GS-MPSO-WKNN is also robust to a high missing data ratio.
In this paper, the problem of global state feedback stabilization for a class of stochastic high-order feedforward nonlinear systems with different power orders and multiple time delays is investigated. A distinct property of the system to be investigated is that the control coefficients are not restricted to 1. By adding one power integrator technique and homogeneous domination approach, a state feedback controller design is recursively proposed, which ensures the global asymptotical stability in probability of the closed-loop system. Finally, a simulation example is given to illustrate the effectiveness of our results obtained in this paper.
A condition parameter degradation assessment and prediction model was developed to evaluate and forecast hydropower units based on the Shepard surface, intrinsic time-scale decomposition (ITD), a radial basis function (RBF) artificial neural network and grey theory. The model includes the effect of the active power and the working head on the hydropower unit’s condition. The condition parameter degradation time series is decomposed into a finite number of proper rotation components and an approximate component using the ITD method. The GM(1,1) model (a first-order one-variable grey model) is then used to predict the approximate component time series. The proper rotation component time series are then predicted separately by building different RBF neural networks. Finally, the original condition parameter degradation time series is found by adding these results. Real condition monitoring data from a pumped storage power station in China is used to verify the method. The results show that this method accurately reflects the condition parameter degradation for the hydropower unit.
This paper presents finite-time hybrid projective synchronization for the unified chaotic system. The significant contribution of this paper is that master–slave system realizes hybrid projective synchronization within a pre-specified convergence time. Base on the Lyapunov stability theory and finite-time stability theory, a finite-time controller is derived to make the state of the slave system exponentially synchronize the state of the master system, and the different components of state of the slave system and the master system synchronize up to different desired scaling constant. Numerical simulations are presented to show the effectiveness of theoretical analysis.
This paper is concerned with a state feedback controller design method for neutral systems with a time-varying delay, considering uncertainties in the plant parameters, as well as in controller gain. The uncertainties are in additive form, affecting both the system matrices of the plant and the controller gain. The uncertainties that are assumed admissible are time-varying and norm-bounded. The neutral system is also subject to external disturbances. A robust stabilizing H-infinity state-feedback controller is synthesized under several conditions that are presented in the form of matrix inequalities. A new generalized type of Jensen integral inequality has been introduced for utilization in the derivation of the aforementioned results, which could thus have been relaxed via that approach. A feasible solution set is obtained using the well-known cone complementarity technique by solving a non-linear minimization problem subject to linear matrix inequalities. A numerical example with case studies concludes the present work. The results of the minimum achievable attenuation rate indicate considerable improvement in comparison with those reported in the literature.
To improve flight performance of small rotary-wing unmanned aircraft under complex environment, a composite control method based on internal model control (IMC) and adaptive radial basis neural network (RBFNN) is proposed. With the analysis of the characteristics of system disturbance, an IMC system is constructed to eliminate system errors. Furthermore, an adaptive RBFNN without prior training is proposed to eliminate residual estimation errors to augment the control performance. The effectiveness of the composite control method is validated by a series of flight tests. Compared with the feedback control method, the composite control method can yield good tracking performance under wind disturbances.
The image reconstruction task in electrical capacitance tomography (ECT) is an ill-posed problem, in which image reconstruction methods play a vital role in real applications. In this paper, a multiple measurement vector-based dimensionality reduction dynamic reconstruction model that simultaneously utilizes the ECT measurement information and the dynamic evolution information of a dynamic object is proposed. A robust sparse orthogonal projective non-negative matrix factorization (RSOPNMF) method is proposed for extracting the basis vectors from a set of snapshots, and the split Bregman iteration (SBI) algorithm is used to solve the RSOPNMF model. The original unknown variables are projected onto the subspaces spanned by a set of the basis vectors extracted by the RSOPNMF method from a set of snapshots to obtain a low-dimensional model, where the images are indirectly reconstructed by solving the corresponding low-dimensional coefficient vector, and thus the dimensionality of the unknown variables is reduced and the computational cost are decreased. Based on the multiple measurement vectors and the dimensionality reduction model, an objective functional that incorporates the ECT measurement information, the dynamic evolution information of a dynamic object, the spatial constraint and the temporal constraint is proposed, in which the unknown variables are solved in a batching pattern. An iterative scheme that integrates the beneficial advantages of the SBI method and the forward–backward splitting algorithm is developed for solving the proposed objective functional. Numerical simulation results validate the feasibility of the proposed algorithm.
The system is often described as a series of blocks linked together in non-linear system identification. Such block-oriented models are built with static non-linear subsystems and linear dynamic systems. This paper deals with the parameter estimation of Hammerstein systems with piecewise non-linearities, which is a blocked-oriented model where a static non-linear blocking is followed by a linear dynamic system. The basic idea is as follows. The key term separation technique is applied initially, and then a corresponding auxiliary model is constructed. Hence, the identification problem of the system is converted to a non-linear function optimization problem over parameter space. Once again, the estimates of all the parameters are obtained by a proposed particle swarm optimization algorithm. Finally, compared with the existing methods, the simulation results confirm that the presented method is valid. Moreover, the presented method is further extended to estimate Hammerstein systems with discontinuity non-linearities.
This paper deals with the power acquisition control of variable-speed wind energy conversion systems under inaccurate wind speed measurements. The control goal is to optimize the power capture from wind by tracking the maximum power curve. Firstly, the controller is designed for the case with known aerodynamic torque, which is a common assumption in many literatures. In this controller, the need for the exact knowledge of the system model is waived by using adaptive technologies. The chattering phenomenon in the generator torque, which can result in high mechanical stress, is avoided by adopting a modified robust term. Then, by utilizing an online approximator to learn an auxiliary term induced by the uncertain aerodynamics, the need for the exact knowledge of the aerodynamic torque is waived. Both of the proposed controllers are capable of providing good performance under inaccurate wind speed measurements. The control objective is obtained in the sense that the tracking error is guaranteed to converge to an arbitrarily small set. It is theoretically proved that all the signals in the closed-loop system are bounded via Lyapunov synthesis. Finally, the performance of our proposed controller is shown by simulating on a 1.5 MW three-blade wind turbine using the FAST (Fatigue, Aerodynamics, Structures, and Turbulence) code developed by the National Renewable Energy Laboratory.
Based on wireless sensor network (WSN) technology and crop growth simulation techniques, this paper shows the design and realization of an automatic monitoring and closed-loop control system in greenhouses. Firstly, a multi-hop network communication method based on clustering and simple medium access control that is suitable for the monitoring of a large-scale greenhouse environment is designed and analysed, and the simulation results show that its lifetime is 10% longer than LEACH (low energy adaptive clustering hierarchy) when 1% and 20% nodes die. Secondly, a physiological development day-based crop growth simulation model will be built to predict the tomato growth and make further decisions in adjusting the greenhouse climate. In order to obtain the model indicators, early experiments were carried out on four kinds of tomato variety, and the experiment results show that the proposed model has a higher accuracy than the effective temperature model on the root mean square error within 1–4 days, and on the mean absolute error within 2–4 days. Finally, according to the proposed methods, a comprehensive greenhouse dynamic monitoring and closed-loop control system with a 60 MC13213 nodes WSN was implemented. The implementation results show that with three AAA Ni–MH (nominal capacity 750 mAh) batteries, 80% nodes maintained a survival time of 45–60 days, and the model prediction compared with the observed value is at a high correlation efficient of 95%.
This article considers a combined pole assignment and multivariable decoupling control algorithm using discrete-time, non-minimum state space (NMSS) methods. In contrast to earlier research based on low-order linear models, the approach is applied to a nonlinear mean value internal combustion engine model with three control inputs, namely the throttle plate angle, injected fuel mass flow and spark advance angle. The controlled outputs are the air mass flow pressure, crank shaft speed and air–fuel ratio (AFR). It is well known that, for example, regulating the AFR to the stoichiometric value (i.e. 14.7) leads to a desirable balance between power output and fuel consumption, while reducing pollutant emissions. In this regard, the linear NMSS approach is straightforward to design for a range of performance requirements and yields comparable results to a more complex benchmark sliding mode control system. Furthermore, it retains a similar implementation structure to current production units, which are typically based on conventional proportional-integral compensation. The robustness to changing operating levels and disturbances, including an air leakage signal, are evaluated in simulation.
Indoor GPS is one of the most popular positioning systems own to its high accuracy, real-time characteristics and multi-task management. In order to simplify the calibration process and extend its application, a single station model was presented recently. This paper proposes a novel ultrasonic ranging method used for the single station model. The traditional high-accuracy ultrasonic ranging method mainly uses a phase detection method by transmitting a multiple-frequency continuous wave. However, this method requires high accuracy of the phase detector and is still limited to small-scale application as a result of applying a continuous wave. Based on the constant time difference between the corresponding zero-cross points, this paper proposes a novel two-frequency pulse wave method, which can estimate the time of flight using the time differences between two received waves. Then a least squares estimation is used to eliminate random errors. Finally, an ultrasonic ranging experiment was conducted to validate its feasibility and stability.
The design of supervisory controllers to resolve any deadlock issue in automated manufacturing systems (AMSs) has attracted the efforts of many researchers. A great deal of work, which assumes that allocated resources do not fail, has been done, while only a few works pay attention to the existence of unreliable resources in AMSs. In this paper, we focus on the robust supervisory control for avoiding deadlocks in AMSs, each of which has one specified unreliable resource. We develop a robust supervisory control policy under which the system can continue producing without manual intervention in the face of the unreliable resource’s failure and recovery. Our policy consists of a modified Banker’s Algorithm and the available resource constraints. After proving the correctness of our policy, we show that there are more reachable states under our policy than the existing one. Therefore, our policy imposes less restrictive constraints on the system and is more permissive than the original one. Finally, an example is provided to illustrate our control policy’s advantage in permissiveness.
Noisy behaviour of time domain passivity control (TDPC) at low velocity is a well known problem for delayed teleoperation systems. This paper presents a novel adaptive model-based passive controller to alleviate some of the noise problems associated with delayed teleoperation system using the TDPC approach. By composing an online estimation of phantom human tissue model and passivity observer on the master side, a high transparency of teleoperation can be achieved while the system passivity is maintained by modifying the damping of the master. The performance of the developed approach was validated using one-degree-of-freedom master–slave robot system with constant time delay. Results show that the phantom tissue parameters can be accurately estimated. In addition, the passivity of the system is observed by the passivity observer using the output of the identified tissue model. Results demonstrate that stable teleoperation with time delay can be achieved and the environment force can be accurately reflected to the operator without chattering.
This paper considers the problem of time-varying force control for robot manipulators in the presence of uncertainties from both the robotic model and working environment. The position-based impedance control (PBIC) method is employed and in order to achieve accurate time-varying force tracking, an improved PBIC is proposed. A neural-network-based robust controller is proposed to compensate for the system uncertainties, and an adaptive law is developed to identify the uncertain environmental parameters. Simulation results on a two-link robot manipulator confirm the effectiveness of the method in achieving time-varying force tracking.
This experimental study investigates the practical benefits and drawbacks of error-cube control for closed-loop PID control structures. The error-cube control approach employs the cube power of the error signal for controllers and this causes variability in control characteristics due to the non-linearity of the cube power operation. The error-cube signal introduces attenuated and magnified error regions. These two characteristic error regions result in a tight control regime and a slack control regime, depending on magnitude of the error signal. The study presents a discussion on non-linear error signals in a practical aspect and demonstrates the effects of non-linear error signals on the step response of closed-loop PID control systems via simulation results and experimental measurements. An enhanced error-cube controller was proposed to improve the control performance of the error-cube control and results are discussed.
This paper presents an adaptive neural-fuzzy control scheme for a dual-level-structure flexible manipulator with variable dynamic payload. The dynamic moving model of the flexible manipulator is derived and the state-space equation is formulated first. A control scheme that consists of a neural-fuzzy controller in the feedback channel and an image-guided identification network (IGIN) in the forward configuration is then proposed. The IGIN is employed to locate the object (e.g. bimetal) to achieve the tracking function, while the dynamic neural network is used to learn the weighting factor of the fuzzy controller. Finally, simulations are run for various modes to describe the dynamic tracking system, and simulated results show a good performance of the control tracking system.
This paper is concerned with numerical solutions to the generalized coupled Sylvester matrix equations
and the periodic coupled matrix equations
which have many applications in several areas, such as control theory, stability theory, signal processing and perturbation analysis. By extending the bi-conjugate gradients (Bi-CG) and bi-conjugate residual (Bi-CR) methods, we obtain effective iterative algorithms for finding the solutions of the generalized coupled Sylvester and periodic coupled matrix equations. In order to compare these new algorithms with some existing methods, we present some numerical examples.
In this paper, we present an algorithm which could be considered an improvement to the well-known Schulz iteration for finding the inverse of a square matrix iteratively. The convergence of the proposed method is proved and its computational complexity is analysed. The extension of the scheme to generalized outer inverses will be treated. In order to validate the new scheme, we apply it to large sparse matrices alongside the application to preconditioning of practical problems.
In active noise control (ANC), the performance of the filtered-x least mean squares (FXLMS) algorithm is degraded by the saturation of the loudspeaker in the secondary path. Predistortion is a linearization technique commonly used in signal processing applications to compensate for saturation nonlinearity. The design of the predistorter (PD) requires the use of direct measurement from the output of the nonlinear element. However, in ANC applications, direct measurement from the loudspeaker output is not available. Therefore, a conventional PD design approach cannot be directly applied. In this paper, a new PD-based compensation technique based on the inverse model of the loudspeaker nonlinearity is proposed. The PD is represented by an approximated memory-less inverse tangent hyperbolic function (ITHF). The approximated ITHF is scaled by a pre-identified parameter, which represents the loudspeaker nonlinearity strength. This parameter can be obtained by modelling the secondary path using a proposed block-oriented Hammerstein structure in which the nonlinear part is represented by a memory-less tangent hyperbolic function (THF). Simulation results show that using the proposed PD along with the FXLMS algorithm increase the noise reduction performance significantly.
In this paper, a mixed method of model order reduction for a continuous-time single-input single-output system is presented. The denominator of a reduced-order model (ROM) is obtained by clustering the poles of the original high-order system using the Fuzzy C-Means clustering technique retaining some dominant poles. Having determined the denominator polynomial, numerator coefficients are found by Padé approximation by matching the desired number of time moments and Markov parameters. The ROM of the proposed method provides good approximation to the original system both in terms of transient and steady-state response.
In this paper, the linearization of product polar quantizers is presented. Product polar quantizers are very important in A/D (analogue-to-digital) conversion and compression of measurement signals with Gaussian distribution, as they have much better performances than scalar quantizers and can be widely used in many modern measurement systems such as telemetry, telemedicine, wireless sensor networks, distributed measurement systems and remote control systems. The key limiting factor in the realization of product polar quantizers is the fact that the companding function for the magnitude quantization is non-linear. To decrease complexity of realization of product polar quantizers, we suggest the linearization of the companding function. Two methods of linearization are proposed. Although both methods are good, we highlight the second method, which achieves high performances with a small number of linear segments, due to optimization of the last linear segment. The convergence of expressions for the distortion before and after linearization is proved. The analysis is developed in the general manner (for any companding function) and applied on a µ-law companding function. The influence of linearization on the robustness of product polar quantizers is analysed. The best solutions for the design of the linearized product polar quantizers, considering complexity and quality, are proposed. The linearization of product polar quantizers compatible with the widely used G.711 standard is performed. It is shown that linearized product polar quantizers, although much simpler, can achieve performances very close to those of the corresponding non-linearized quantizers. Theory is proved by the simulation and experiment.
Although distributed model predictive control (MPC) has received significant attention in the literature, the robustness of distributed MPC with respect to model uncertainties and state delays has not been explicitly addressed. In this paper, a novel approach to design robust distributed MPC is proposed for polytopic uncertain systems with state delays. The algorithm requires decomposing the entire system into M subsystems and solving M linear matrix inequality optimization problems to minimize an upper bound on a robust performance objective for each subsystem. An iterative on-line algorithm for robust distributed MPC is developed to coordinate the distributed controllers. The algorithm is a flexible structure of robust control, which allows the independent computation of the state feedback laws for the subsystems. Convergence and robust stability of the proposed distributed MPC are analysed. Two numerical examples are carried out to demonstrate the effectiveness of the proposed algorithm.
An adaptive Huber-based Kalman filter (AHF) is presented to deal with model error and unknown measurement noise in this article. The adaptive method for model error is obtained using an upper bound for the predicted state error covariance matrix. The measurement noise uncertainty is tackled at each time step by minimizing a criterion function that is original from the Huber technique. A recursive algorithm is also provided to solve the criterion function. The proposed AHF algorithm has been tested in an attitude estimation problem using a gyroscope and star tracker sensors for a single spacecraft in flight simulations in the presence of both model error and non-Gaussian random measurement errors. Simulation results demonstrate the superior performance of the proposed filter compared with the previous filter algorithms. The main contribution of this work can be considered the new application of an existing method.
This paper studies the problem of vision-based adaptive control for robot manipulators under a fixed camera configuration, when the camera intrinsic and extrinsic parameters are uncertain. An image-based controller, which requires the image Jacobian for its implementation, is conducted. The image Jacobian is inversely proportional to the depth factor. Hence it is not linearly parameterizable and the depth factor cannot be adapted with uncertain camera parameters. To cope with the inverse dependence of the image Jacobian on the depth factor, the controller employed the polynomial interpolation to parameterize the image Jacobian matrix linearly. The coefficients of the polynomial are obtained by a trained adaptive-network-based fuzzy inference system. A stability analysis for the proposed method is provided by a full consideration of the non-linear dynamics of the robot manipulator. Simulation results are presented to demonstrate the effectiveness of the proposed approach.
Transient aerodynamic heating experiments with high-speed aircraft require temperature sensors that can carry out rapid and accurate electromotive force (EMF)–temperature conversions. A fast, high-precision non-linear EMF–temperature conversion method is proposed. In this method, the temperature values to be converted were pre-positioned using a non-linear mathematical model. Then, they were accurately positioned using an efficient binary search algorithm with a small scope. Thus, this method has rapid conversion speed and high calibration precision. This conversion precision is enhanced by one order of magnitude over that of the normal reference table, and the conversion time is 1% of that of the traditional piecewise linearization method. This method was employed in a transient aerodynamic heating experimental simulation system with high-speed aircraft. The experiment results show that, in the case of a high change rate of temperature and heat flux, accurate dynamic tracking can still be realized, and the experimental simulation results agree well with the pre-set environment. The developed temperature sensor calibration method is necessary for high-speed and high-precision aerodynamic heating experiments with hypersonic aircraft.
A supply chain management system is defined as communications among suppliers, plants, distribution centres, retailers and demand stimulus. These systems are large scale and multi-agent, and therefore a decentralized control method must be used. Also, demand forecasting, as a challenge in production management, can be estimated by advanced methods or modelled using demand forecasting functions. In this paper, a new decentralized receding horizon control method is used to achieve customer contentment and low-cost inventory in a complete chain of supply, manufacture, assembly, warehouse, distribution and retail units. The main novelty of the method returns to the use of both the move suppression term and the look-ahead idea to increase robustness and smoothness in a supply chain containing assembly units. Also, a Kalman filter estimator is applied to estimate states and output variables. For this purpose, a suitable model and appropriate optimal control method are developed. Finally, the efficiency is indicated regarding simulation results.
In practice, IOT (Internet of things) gateways are often used between sensor networks and the Internet to provide advanced services such as device monitoring and control. Sensor networks are connected to the Internet via these gateways based on various transmission protocols. In particular, the main features of IOT gateways are reliability, high real-time, security and so on. This paper proposes a heterogeneous IOT gateway based on dynamic priority scheduling algorithm. This gateway realizes data conversion and transformation between the Internet and sensor networks as well as several kinds of communication protocol: RS485, Bluetooth, CAN, Zigbee and GSM. To ensure the data security and reliability for IOT gateway, some higher-level protocols are designed and implemented on the gateway. A dynamic priority scheduling algorithm of a real-time system is also used in the gateway to address the problem of data concurrency and improve real-time performance by efficiently scheduling tasks. Simulation results reveal that the gateway realizes the data transmission between sensor networks and the Internet using specific higher-level protocols and response within a very short delay, achieving the goal of addressing the problem of data concurrency.
In the present paper, by using of the coefficients of the characteristic polynomial of matrix B and the so-called Leverrier algorithm, the explicit solutions to the Sylvester-conjugate matrix equation AX – XB = C (including the Lyapunov-conjugate matrix equation as special case) have been constructed. While one of the explicit solutions is stated as a polynomial of coefficient matrices of the matrix equation, one of the explicit solutions is expressed by the symmetric operator matrix, controllability matrix and observability matrix. Comparing to the existing results, there is no requirement on the coefficient matrices. At the end of this paper, one numerical example is shown to illustrate the effectiveness of the proposed method.
Research on swarm robotics has become increasingly important in the last three decades. One of the major research directions in swarm robotics is the control of the formation of swarm robots to perform various tasks in 2D or 3D environments. In this paper, we propose a new planar (2D) formation control framework based on a dynamic version of elliptic Fourier descriptors (EFDs) and develop formation controllers that enable robots to form the desired shapes. The main advantage of this method over more traditional approaches in the literature is its flexibility in the representation of the desired shape. The formation can be virtually any arbitrary planar closed curve. Representation by EFDs becomes much more powerful as the number of harmonics in the representation increases. Performance of the proposed formation controller is tested in three simulations where desired swarm formations modelled by EFDs with two, three and six harmonics are considered. Results are quite promising.
The design problems using multidisciplinary parameterization methods for a hypersonic morphing vehicle are investigated in this paper. First, the non-linear model of the hypersonic morphing vehicle in line with the parameterized process is established by combining with the different disciplines. Then, the waverider performances determined by the static and dynamic properties are discussed by application of the multidisciplinary design ideas. Afterwards, the optimal results considering the anticipated performance criteria are acquired by multidisciplinary optimization. Furthermore, the switching control system is designed for hypersonic morphing vehicle to implement the smooth morphing process and to guarantee the overall stability. Finally, the proposed methods are verified by a numerical example, and the resulting simulation shows that waveriding performance can be enhanced as the hypersonic vehicle is designed as a morphing shape.
Recently, Ramadan et al. have focused on the following matrix equation:
A 1 V + A 2 V + B 1 W + B 2 W = E 1 VF 1 + E 2 V F 2 + C
and propounded two gradient-based iterative algorithms for solving the above matrix equation over reflexive and Hermitian reflexive matrices, respectively. In this paper, we develop two new iterative algorithms based on a two-dimensional projection technique for solving the mentioned matrix equation over reflexive and Hermitian reflexive matrices. The performance of our proposed algorithms is collated with the gradient-based iterative algorithms. It is both theoretically and experimentally demonstrated that the approaches handled surpass the offered algorithms in the earlier referred work in solving the mentioned matrix equation over reflexive and Hermitian reflexive matrices. In addition, it is briefly discussed that a one-dimensional projection technique can accelerate the speed of convergence of the gradient-based iterative algorithm for solving general coupled Sylvester matrix equations over reflexive matrices without assuming the restriction of the existence of a unique solution.
This work presents novel parallel biologically inspired hybrid heuristics for task scheduling in distributed heterogeneous computing and grid environments, and NP-hard problems with capital relevance in distributed computing. Firstly, sequential hybrid metaheuristics based on artificial immune systems (AIS) are developed to provide a good scheduler in reduced execution time and improved resource utilization. In the new AIS, affinities of the antibody’s genes are also effectively evaluated and regarded as memes from population real-time evolution; self-organized gene–meme co-evolution is simulated to improve population convergence; and appropriate Lyapunov functions inspired by interactive activation and competition neural networks are constructed to balance exploration and exploitation. Secondly, parallelization of the AIS-based algorithm is hierarchically designed and integrates with the two traditional parallel models (master–slave models and island models). The method has been specifically implemented on the newly developed supercomputer platform of hybrid multi-core CPU+GPU using C-CUDA for solving large-sized realistic instances. Numerical experiments are performed on both well known problem instances and large instances that model medium-sized grid environments. The comparative study shows that the proposed parallel approach is able to achieve high solving efficacy, outperforming previous results reported in the related literature, and also showing good scalability behaviour when facing high-dimension problem instances.
This paper proposes a new method to design a reinforcement learning-based integrated kinematic and dynamic tracking control algorithm for a non-holonomic wheeled mobile robot without knowledge of the system’s drift tracking dynamics. The actor critic structure in the control scheme uses only one neural network to reduce computational cost and storage resources. A novel tuning law for a single neural network is designed to learn an online solution of a tracking Hamilton–Jacobi–Isaacs (HJI) equation. The HJI solution is used to approximate an H optimal tracking performance index function and an intelligent tracking control law in the case of the worst disturbance. The laws guarantee closed-loop stability in real time. The convergence and stability of the overall system are proved by Lyapunov techniques. The simulation results on a non-linear system and wheeled mobile robot verify the effectiveness of the proposed controller.
The observer-based finite-time passive control problem of a class of Lipschitz nonlinear systems with uncertainties and time delays is studied. The nonlinear parameters are considered to satisfy the global Lipschitz conditions and the exogenous disturbances are unknown but energy-bounded. By constructing an appropriate Lyapunov function, a observer-based state feedback controller is designed to guarantee that the resulting closed-loop system is finite-time bounded and satisfies the given passive constraint condition. Some sufficient conditions for the solution to this problem are derived in terms of linear matrix inequalities. Simulation results illustrate the validity of the proposed approach.
This paper proposes a boundary feedback control design for open canal networks using the linearization of boundary conditions. For open canal networks with any types of cross-sections, which can be modelled by the Saint-Venant equations, the characteristic form in terms of Riemann invariants has been established. Under this established characteristic form, the stabilizing boundary control law has been developed by linearizing the boundary conditions for both a single reach and the open-channel network composed by multi-reaches in a cascade. The design of the boundary feedback control laws for both a single canal and the cascaded networks is illustrated in a unified framework, which extends the results in the literature.
This paper addresses the robust switching tracking neural control problem for a robotic manipulator in the presence of uncertainties and disturbances. The proposed controller, which is a combination of a robust adaptive control technique, radial basis function neural network approximation and average dwell-time technique, can guarantee position tracking performance of robotic manipulator system, in the sense that all variables of the resulting closed-loop system are bounded and the H disturbance attenuation level is well obtained. Simulation results on a two-link robotic manipulator show the satisfactory tracking performance of the proposed control scheme even in the presence of large modelling uncertainties and external disturbances by comparing it with PD control strategy.
This paper is concerned with the analysis of the stability of delayed recurrent neural networks. In contrast to the widely used Lyapunov–Krasovskii functional approach, a new method is developed within the integral quadratic constraints framework. To achieve this, several lemmas are first given to propose integral quadratic separators to characterize the original delayed neural network. With these, the network is then reformulated as a special form of feedback-interconnected system by choosing proper integral quadratic constraints. Finally, new stability criteria are established based on the proposed approach. Numerical examples are given to illustrate the effectiveness of the new approach.
A novel online final product quality prediction scheme is proposed in this paper for the improvement of quality prediction in multi-phase batch processes. Phase cumulative product quality (PCPQ), which is quality cumulated from the beginning of the phase to the end, is introduced for quality prediction, and final product quality prediction offline is achieved by cumulating all the predicted PCPQ based on the corresponding PCPQ model. In this way, the quality prediction approach proposed not only explores the different effects of process variables in different phases on final product quality, but also takes the common effects of process variables in different phases into account. The PCPQ model and remained phase cumulative product quality (RPCPQ) model are combined to improve online prediction precision, without the missing observations estimation. The proposed approach is applied to a simulated penicillin fermentation process and the results of simulation demonstrate effectiveness and superiority.
In this paper, we studied the peak-to-peak output feedback control problem for a linear discrete-time system with input and output static quantizers, which are bounded by sectors. Firstly, the quantized peak-to-peak control issues are addressed with a robust problem by two sector-bound conditions. Secondly, by using linear fractional transformation (LFT) techniques, the closed-loop control system is expressed as an uncertain system with the LFT uncertainties. Then, we present sufficient conditions to design a peak-to-peak output feedback controller to mitigate quantization effects, and ensure a prescribed peak-to-peak noise attenuation level. Finally, two numerical examples are given to verify the effectiveness of the main results.
In this study, a velocity sharing historical best particle swarm optimization algorithm (VSHBPSO) and its variants are proposed to improve the performance of the original particle swarm optimization (PSO). The shared information in the improved algorithms includes the historical best position of each particle searched in the previous experiments, the updated velocity and the present global best position. An orthogonal design trial is conducted to discuss the parameters of the proposed algorithms by using 10 non-linear functions with different dimensions. Furthermore, the performance of the new algorithms is evaluated. Experimental results show that the novel algorithms can derive better solutions than the PSO algorithm and indicate their effectiveness in optimizing non-linear functions. Finally, the proposed algorithm is applied in soft sensing the outlet ammonia content in the ammonia synthesis process. The VSHBPSO-based soft sensor is found to be effective in the real-time assessment of ammonia content.
The H static output feedback control of continuous Markov jump systems with incomplete transition probabilities is investigated in this paper. Employing the property of continuous transition probabilities and a matrix transformation technique, relaxed sufficient conditions for the existence of a solution to the H static output feedback control problem are established in terms of linear matrix inequalities. In addition, the obtained results are extended to Markov jump nonlinear systems. Numerical examples are provided to demonstrate the effectiveness of the derived results.
The accuracy and stability of electronic transformers (ETs) is the guarantee of the safe and reliable operation of a power system. As an emerging technology, however, the ETs’ failure rate is higher than that of traditional transformers. As a result, the calibration period should be shortened. Existing calibration methods address the verification on condition that the ETs and lines are powered off, which has a long calibration period and results in a power-off loss. As a result, an online calibration system for electronic voltage transformers (EVTs) is proposed in this paper. Employing a specially designed standard voltage online measuring unit, the proposed system can obtain the high voltage signals without powering off and do the calibration when the EVTs are working. The power-off loss can be avoided, and problems of the running EVTs can be detected in time. The main factors influencing the system accuracy are addressed. The system has been validated by the Quality Inspection and Testing Center of the China Electric Power Research Institute, and field tests have been conducted in Guizhou Electric Power Company. The results show that the proposed system has good reliability and can reach an accuracy class of 0.05 in complicated electromagnetic circumstances.
In order to make the dynamic voltage restorer (DVR) concurrently compensate for low-order harmonics and voltage sag, and eliminate the influence of digital control on system performance, we propose a novel double closed-loop digital control strategy, consisting of the fundamental proportional resonant (PR) control in a voltage loop and selective harmonic PR control in an inductance current loop. Then, we mainly analyse the discretization effects of the virtual LC method and the step response method, and further present a straightforward digital design method. Next, with this method, we design the parameters of the fundamental and selective harmonic PR controllers in the discrete domain, which inhibit the influence of sampling, calculation delay etc. on the steady-state error and the dynamic response performance. Finally, an 11-kVA DVR prototype is developed and tested. The experimental results indicate that the proposed control strategy satisfies the requirement of voltage quality for sensitive loads and achieves a good dynamic response performance.
A large number of applications in industrial domains are concerned to slow processes, including flow, pressure, temperature and level control. Synthesis and control of such applications in the presence of uncertainty have been studied by many authors in recent decades. In this paper, a robust flow controller has been designed using H techniques to define and regulate the flow of pipe. The most robust method is synthesized using a state-space approach by taking into account the structured (parametric) perturbations in the plant coefficients. The proposed approach ensures internal stability, satisfying both frequency and time domain requirements, and obtaining minimal performance H -norm of the closed-loop system. The simulations are carried out using MATLAB and the results are compared with the classical PID, linear–quadratic–Gaussian and H 2 methods, and they indicate that the overall system output performance can be improved using the proposed H robust controller.
This paper investigates the finite-time H control problem for discrete-time Markovian jump linear systems with time delay and norm-bounded exogneous disturbance. The stochastic finite-time boundness is achieved by employing an average dwell time approach, which has more design freedom by allowing the stochastic Lyapunov-like function (SLLF) to increase more slowly during the running time of each operation mode. Note that the SLLF increases at every jumping instant rather than at every sampling time instant. Sufficient conditions for the solvability of the controller, which are dependent or independent on the time delay, are given in terms of linear matrix inequalities. Numerical examples are given to verify the efficiency of the proposed method.
This paper presents the development, stability analysis and validation of an intelligent proportional integral (iPI) controller for the tip position control of a flexible-link manipulator. A stability analysis included in the paper shows that the iPI controller is equivalent to the proportional integral-squared controller. In order to verify the performance of the iPI controller, several experiments were conducted. In these experiments, step and square-wave inputs and two other trajectories were applied to the flexible-link manipulator. Also, the performance of the iPI controller was compared with those of classical PI and PID controllers. The results obtained from the comparison experiments showed that, the PI and PID controllers produced better performance in step and square-wave inputs, but the iPI controller yielded better trajectory tracking performance. All of the controllers were tested for disturbance and noise rejection capability. The iPI controller eliminated disturbance and noise better than the classical controllers. Considering all of the results, the iPI controller has great potential in trajectory tracking control of flexible-link manipulators.
This paper investigates the design of a composite nonlinear feedback (CNF) control law for an overhead crane servo system to improve the transient performance of both displacement tracking of the trolley and anti-sway of the payload. To address the property of underactuation of the overhead crane system, a novel nonlinear function of the CNF control law is specifically proposed to compromise the tracking performance of the trolley and the anti-sway performance of the payload. The performance improvement in both tracking of the trolley and anti-sway of the payload is illustrated with a complete comparison between the CNF control method and the trajectory planning method, which has been proposed in recent literature. The simulation results show that this well-tuned CNF control law can significantly shorten the settling time of the trolley displacement tracking and reduce the sway of the payload.
This article demonstrates an iterative approach for precise parallelism and gap control of two platforms. In order to achieve this goal, three linear actuators were used to adjust the posture and distance of one platform with respect to the other. Besides, motion coupling between three actuators was considered and compensated for by a model-based iterative control method. The features of the proposed method are a simple actuating mechanism, efficient sensing architecture, and robust iterative control for permissive modelling uncertainties. Experimental results show that, within 0.3 s, the desired parallelism and gap can be achieved with errors in the sensor resolution level. Therefore, the proposed method is suitable for precise parallelism and gap control of two platforms.
The optimal tracking performance of single-input-single-output networked control systems over limited communication channels is proposed in this paper. The signal-to-noise ratio (SNR) constrained of communication channel is considered. The tracking performance is measured by the energy of the error variance response between the output of the plant and the reference signal. The optimal tracking performance is obtained by applying the H 2 square error criterion and the spectral factorization technique. It is shown that the optimal tracking performance is constrained by the non-minimum phase zeros, the unstable poles of a given plant, the power spectral density of a given reference signal, and the SNR of a communication channel. The results obtained in this work explicitly show how the optimal tracking performance is limited by the communication parameters (SNR in this paper). Finally, computer simulations are performed to verify the analytical results.
This paper addresses the problem of non-fragile robust optimal guaranteed cost control for a class of two-dimensional discrete systems described by the general model with norm-bounded uncertainties. Based on Lyapunov method, a new linear matrix inequality (LMI)-based criterion for the existence of non-fragile state feedback controller is established. Furthermore, a convex optimization problem with LMI constraints is formulated to select a non-fragile robust optimal guaranteed cost controller, which minimizes the upper bound of the closed-loop cost function. The merit of the proposed criterion in aspect of conservativeness over a recently reported criterion is demonstrated with the help of illustrative examples.
This paper deals with the consensus problems of multi-agent systems with nonlinear dynamics and sampled data information. The control input of each agent is based on the information of its neighbors at discrete sampling instants rather than the whole continuous process. An input delay approach is utilized to transform the sampling data into the time-varying delayed data. A novel time-dependent Lyapunov functional consisting of the continuous term and the discontinuous term is proposed. Tools like matrix theory, algebraic graph theory, relaxed matrix approach, and convex analysis technique are utilized to derive the sufficient conditions. The estimated upper bound of the sampling interval can be obtained by the proposed conditions. Then, the necessary and sufficient conditions for consensus in systems with linear dynamics are presented. It is shown that the consensus conditions depend on the parameters of the sampling interval, spectra of the Laplacian matrix, and coupling strength. The effectiveness of the proposed design method is demonstrated by the simulation examples.
This paper deals with the problem of state feedback stabilization with finite-time stochastic stability for a class of discrete-time switched stochastic linear systems under asynchronous switching. The attention is focused on designing the feedback controller that guarantees the finite-time stochastic stability of the dynamic system. The finite-time stochastic stability definition of discrete-time switched stochastic systems is introduced. The asynchronous switching idea originates from the fact that switching instants of the controllers lag behind or exceed those of subsystems. On the basis of the average dwell time method and multiple Lyapunov functions approach, a finite-time stochastic stability condition is established. Then, an asynchronously switched controller is designed and the corresponding switching law is derived to guarantee the considered system be finite-time stochastically stable. Two numerical examples are provided to show the effectiveness of the developed results.
An effective method is presented to compute the loop gain margins exactly for two-input–two-output processes. The stable regions in parameter space are first obtained by determining the stability boundaries and the loop gain margins found within the stable regions. Examples are provided for illustration and comparison with other methods.
In this paper, a time-varying nonsingular terminal sliding mode (T-NTSM) controller is proposed and modified for the rigid robot manipulators with parametric uncertainties and external disturbances. First, in order to eliminate the reaching phase, a novel T-NTSM manifold is proposed by incorporating a piecewise defined function of time into a nonsingular terminal sliding mode manifold. Then a T-NTSM controller is derived from such a sliding surface, by which the robustness is ensured during the entire response of the system, and the convergence time can be chosen in advance. An especially effective method is provided for parameter selection to meet the convergence time requirement. Subsequently, a modified T-NTSM controller is proposed to enhance performance by introducing a time-varying gain in the proposed T-NTSM manifold. The modified controller ensures faster convergence rate and smaller control input amplitude. Finally, the proposed controllers are applied in the control of a two-link manipulator. All of the simulation results demonstrate the effectiveness of the proposed control methods.
This paper investigates a leader-following formation control problem for second-order multi-agent systems with nonuniform time-varying communication delays under directed topologies. We first propose a consensus protocol and give a sufficient condition for second-order consensus of the system. Then, under a framework of multiple leaders, the protocol is applied to the formation control, including time-invariant formation and time-varying formation as well as time-varying formation for trajectory tracking. It is shown that the agents will attain the desired formation under the protocol. Finally, several simulations are conducted to illustrate the effectiveness of our theoretical results.
In view of a kind of singularity plants with time delays, a new decoupling internal model control method is first proposed based on pseudo-feed-forward decoupling in this paper, which opened up a new study direction for internal model control. A pseudo-feed-forward decoupling is first designed to decouple the system into an open-loop diagonal dominant system. Rather than performing complex and lengthy calculations, this decoupling method is much better for simplifying the design and calculation. Then the diagonal elements of the decoupled plant are used as an internal model to design a multi-loop internal model controller. The proposed method is also verified applicable for non-singular plants. In order to improve control precisions, the diagonal filter is designed with new Luus–Jaakola (NLJ) optimization algorithm. During the optimization process, the integral of absolute error is adopted as an evaluation index. Finally, two industrial processes are taken as the simulation objects to demonstrate the validity and feasibility of the new method. Simulations results illustrate that the proposed strategy not only avoided complicated computations, but also achieved good trade-off between performance and robustness, even when the system model is mismatched.
This paper presents a new low-cost method for calibration of microelectromechanical system accelerometers. A guide way with low friction that can provide a one-directional linear transversal motion is used. For collecting data sets, the accelerometer is moved manually from one reference point to another, and this movement is measured. A function is developed that relates this movement to acceleration. To verify the proposed calibration method, a three-axis table is used to collect data sets and a least-squares algorithm is applied to find the appropriate function. With this low-cost method, the scale factor matrix, the sensitivity matrix, the non-linear scale factor matrix and bias vector are found.
This paper is concerned with the controllability of a class of switching control systems in which the zero control is an extreme point of the control constraint set. Sufficient and necessary conditions for local controllability at the origin of such systems are presented. In addition, sufficient conditions for local controllability at a nonzero state of such systems are established. Finally, we use the obtained results to study the controllability of the Kinetic Battery Model.
Optimal control of large-scale uncertain dynamic systems with time delays in states is considered in this paper. For this purpose, a two-level strategy is proposed to decompose the large-scale system into several interconnected subsystems at the first level. Then optimal control inputs are obtained by minimization of convex performance indices in presence of uncertainties, in the form of states and interactions feedback. The solution is achieved by bounded data uncertainty problems, where the uncertainties are only needed to be bounded and it is not required to satisfy the so-called ‘matching conditions’. At the second level, a simple substitution-type interaction prediction method is used to update the interaction parameters between subsystems. An iterative two-level algorithm is proposed to coordinate their solutions and achieve the optimal solution of the overall large-scale system. Applicability and performance of the proposed algorithm is shown through simulation of a two coupled inverted pendulums.
This paper proposes an iterative method based on the conjugate gradient method on the normal equations for finding the generalized bisymmetric solution X to the system of linear operator equations
where A1, A2, ..., A1 are linear operators. By the iterative method, the solvability of this system over the generalized bisymmetric matrix X can be determined automatically. When the system of linear operator equations is consistent over the generalized bisymmetric matrix X, the iterative method with any generalized bisymmetric initial iterative matrix X(1) can compute the generalized bisymmetric solution within a finite number of iterations in the absence of roundoff errors. In addition, by the proposed iterative method, the least Frobenius norm generalized bisymmetric solution can be derived when a special initial generalized bisymmetric matrix is chosen. Finally, two numerical examples are presented to support the theoretical results of this paper.
The purpose of this paper is to present a novel combined design of the voltage source converter–high voltage direct current (VSC-HVDC) and the power system stabilizer (PSS) controllers to obtain a better dynamical response. The proposed technique is applied to enhance the damping of the power system low-frequency oscillations (LFOs) and results are compared with traditional design. A fuzzy logic controller is designed for PSS (FPSS). Then, a chaotic optimization algorithm, which has a strong ability for finding the most optimistic results, is employed, in presence of FPSS, to search for optimal VSC-HVDC output feedback controller parameters. Moreover, a singular value decomposition method is utilized to select the most effective damping control signal of the VSC-HVDC output feedback controllers. The novel proposed controllers are evaluated on an AC/DC power system. The simulation results demonstrate that the combined controllers have an excellent capability for damping power system LFOs and greatly enhance the dynamic stability of the power system. Also, the system performance analysis under different operating conditions and some performance indices show the effectiveness of the proposed controllers. The benefit of the suggested procedure is greatly improving the dynamic response of the system. In addition, the overshoots, undershoots and the settling times are dramatically reduced by applying the proposed method.
In system theory and control, the stability of a given system is an important specification; often we design controllers with stability as the highest priority. A computationally faster algorithm for the Routh–Hurwitz criterion has been discussed in our previous work. This article focuses on computationally fast algorithms and simpler proofs of stability criteria for finite-dimensional linear time invariant (FDLTI) systems. Lyapunov introduced his famous stability theory for both linear and nonlinear systems. In this article, instead of solving the Lyapunov equation and checking its solution for sign-definiteness, we present a new way of testing stability. We rewrite the characteristic polynomial in state-space form, controller canonical form in particular, and check the negative-definiteness of the resulting non-symmetric system matrix A. We demonstrate that this approach provides a bridge between the classical approaches and more modern Lyapunov theory as far as FDLTI systems are concerned.
In-vehicle information provision services have started according to the progress of data communication infrastructure surrounding vehicles. In such information services, a large amount of data related to vehicles and drivers has been accumulated in the data centre and been analysed to provide proper information to drivers. Towards such technical trends surrounding vehicles, a cyber physical system for vehicle applications is proposed here. In the proposed system, the expected continuous spiral information flow for vehicles and drivers is described. In the data centre, accumulated information has been analysed by intelligent information processing of data mining, then finally extracted information has been provided to drivers through a human–machine interface. According to the progress of information processing technologies surrounding vehicles, the current situation and challenges of typical technological areas to realize the system are discussed. Several research activities towards potential new services for drivers are also introduced, mainly based on personal adaptation, big data analysis and web mining.
Driver drowsiness greatly increases the driver’s risk of a crash or near-crash. It is recognized as one of the major causes of severe traffic accidents. In this paper, a novel non-intrusive surveillance system is proposed to estimate driver drowsiness by fusion of visual information about lane and driver with Dempster–Shafer theory. Based on expert knowledge and data statistics, various visual features extracted from lane and eye tracking are analysed for their correlation with driver drowsiness in the framework of the subjective ‘observer rating of drowsiness’. The system is validated in real road scenarios and the experiment results demonstrate that it is promising in improving the robustness and temporal response of driver surveillance in real time.
In this paper, the design methods of decentralized PID controllers based on decoupled subsystems are proposed for two-input two-output systems. The higher-order decoupled subsystems are reduced into simple dynamics such as first-order plus dead-time or second-order plus dead-time and the dominant poles are placed at desired locations. The well tuned parameters of decentralized PID controllers can be obtained based on the movement of poles to get the desired closed-loop response of the system. A corollary derived from the generalized Nyquist stability theorem is used to ascertain the nominal system stability and to hold robust stability in the presence of the process multiplicative uncertainties. Finally, two simulation examples are provided for the validity and effectiveness of the proposed design methods. It can be observed that the high closed-loop performance is obtained using the proposed methods and it is comparable to recent methods available in the literature.
Predication of large-scale grid-connected wind power is important for a power system operation’s stability and security. To avoid the problem of being excessively dependent on reference samples, cross entropy theory-based information fusion technology is utilized to set up a new combination predication model for wind power prediction. In this new model, wind power prediction is regarded as an information fusion problem, and the weights of each predication method are dynamically determined by the cross degree of prediction methods obtained by cross entropy theory. The case studies of a practical wind farm validate the effectiveness and correctness of the proposed method to predict wind power and the design of the power dispatching scheme.
In this paper, a cart-type inverted pendulum is controlled using combining of two methods of approximate feedback linearization and sliding mode control. Both position of the cart and angular position of the pendulum are stabilized. Obtained control gains are optimized by a hybrid algorithm based on the particle swarm optimization and genetic algorithm.
This research targets a problem of planar area size inconsistency after image warping to the top view. An orientation sensor attached to a camera was used to acquire the camera’s orientation in real time. Then, extrinsic parameters derived from the sensor and pre-computed intrinsic parameters were used to generate a homography matrix. The homography matrix was modified to eliminate the effect of an angle change, by avoiding the use of shift parameters. In doing so, an area size stays the same regardless of any change in such an angle. A separately required translation matrix was used to shift the top view image. In the shifted top view image, the number of target pixels can be directly counted. Finally, the number of pixel was converted to an area size in real-world units.
This paper presents the problem of identifying a one-place unbounded Petri net. Given an unlabelled graph that represents the modified coverability graph of a net, we establish a Petri net model whose modified coverability graph is isomorphic to the unlabelled graph, and that can identify the weight of the arcs that cannot be obtained from the coverability graph of the net. Based on the partition of the nodes in the unlabelled graph, we guess and decide the structure of a Petri net and the weight of the arcs by an integer linear programming problem. The unknowns to be determined are the elements of the pre- and post-incidence matrices and the initial marking of the net, which can be computed by solving an integer linear programming problem. Finally, an example is used to validate the rationality and effectiveness of the proposed approach.
This paper addresses the problem of global output feedback stabilization for a class of uncertain nonlinear systems subject to time delay. Using the homogeneous domination approach, we first construct a homogeneous output feedback controller with an adjustable scaling gain. With the aid of a homogeneous Lyapunov–Krasovskii functional, the scaling gain is adjusted to dominate the time-delay nonlinearities bounded by homogeneous growth conditions and render the closed-loop system globally asymptotically stable. As a special case, a linear output feedback controller with a tunable scaling gain is constructed to globally stabilize time-delay systems under a linear growth condition. In addition, we also show the proposed approach is applicable to the time-delay systems under lower-order growth conditions and non-triangular growth conditions.
This paper investigates camera control for capturing bottle cap target images in the fault-detection system of an industrial production line. The main purpose is to identify the targeted bottle caps accurately in real time from the images. This is achieved by combining iterative learning control and Kalman filtering to reduce the effect of various disturbances introduced into the detection system. A mathematical model, together with a physical simulation platform is established based on the actual production requirements, and the convergence properties of the model are analyzed. It is shown that the proposed method enables accurate real-time control of the camera, and further, the gain range of the learning rule is also obtained. The numerical simulation and experimental results confirm that the proposed method can not only reduce the effect of repeatable disturbances but also non-repeatable ones.
In this paper, an online prognostics framework is proposed for a class of uncertain non-linear discrete-time systems with multiple faults affecting the system state with all the states being considered measurable. Multiple faults imply that each system state is affected by several faults at the same time provided the faults are separable. In this framework, multiple faults (incipient or a combination of incipient faults) are detected by using the proposed fault detection (FD) estimator, which consists of an online approximator in discrete time and a robust adaptive term. Subsequently, the fault isolation (FI) module is initiated such that each state of the FI observer corresponds to a particular fault type in the case of single fault or fault combination in the case of multiple faults. The faults will be isolated successfully when the corresponding FI state residuals converge to zero in contrast with other FI schemes where they guarantee only boundedness. In addition, multiple isolation estimators are not required here since a decision scheme is utilized by using FD and FI estimators to determine the fault location, type and number of faults that occurred. Suitable mathematical conditions are derived to show the class of faults that could be isolated. Time to failure is determined by using the parameter update law of the FI estimator and the failure thresholds. Finally, a simulation example is used to demonstrate the proposed prognostics scheme.
In this paper a systematic observer-based multiple-model adaptive controller design method is proposed for Lipschitz nonlinear systems. By introducing a compensator in the observer-based controller, the uncertainty due to the estimation error is decreased and the steady-state response is improved significantly. In order to deal with the uncertainty of system dynamics, a multiple-model switching scheme is introduced to improve the transient performance. A state-dependent dwell-time-based switching logic is used to ensure the asymptotic stability as it can cancel the possible increase of Lyapunov function in each switching. A simulation result is given to demonstrate the effectiveness of the proposed method.
In this paper, a novel second-order integral sliding mode control (SOSMC) algorithm is proposed to accomplish velocity control of the permanent-magnet synchronous motor (PMSM) so that the performance can be improved. An integral manifold is utilized to reduce the static error during the sliding mode movement phase to improve the control precision, and a new SOSMC law is achieved by a Lyapunov function approach so the system convergence is guaranteed. The presented method can not only eliminate the system chattering problem successfully but also improve the performance of the PMSM control system. Meanwhile, in order to solve the problem of the windup phenomenon of the PMSM control system, an anti-windup control method is proposed in the PMSM control system. The simulation experimental results are given to indicate that the proposed method is effective and can improve the performance of the PMSM control system such as fast response, high robustness and speed tracking precision, etc.
Much of what is known about the gait-generated method is based on specified gait pattern. Yet in the wild, animals can move over terrains with widely varying properties in various gait patterns. Previous studies have realized gait types such as trot or walk in various legged robots, but no detailed relationship descriptions of different gaits are currently available. This paper addresses the intrinsic relationship between all kinds of gait for legged robot locomotion in order to realize the gait transition smoothly. Based on their relations, the proposed gait-generated method is constructed by a support factor and a delay factor, which describe locomotion efficiency and leg difference motion, respectively. A gait ring concept is proposed to explain their intrinsic relation, and a gait algorithm is proposed to realize the speed transition for legged robot locomotion. A quadruped robot called MATA_ I with 12 DOFs is specially designed for realizing gait transition. This kind of robot has a 4RRRS parallel motion structure, and each joint is driven by motor modules. The algorithm with smooth transition between different gaits was applied in this kind of robot. The simulation results have shown that the proposed gait-generated algorithms can effectively realize gait transition for legged robot.
This paper addresses the problem of the locally stabilizing state-feedback control design for certain nonlinear quadratic systems subject to both actuator saturations and disturbance attenuations. The ideal control law consists of two parts: the main control part is designed to reduce the restricted L2 gain which results from the mismatched disturbance in the controlled output, and the secondary control restrains the degradation of the disturbance attenuation performance which generates from the matched disturbance. Obviously, the proposed control law prevails over the conventional ones in dealing with the disturbance attenuation performance. Constructive conditions based on linear matrix inequalities are provided to ensure the local stability of the objective systems. Simulation examples are given to illustrate the effectiveness of the proposed methodology.
An nxn real matrix P is said to be a symmetric orthogonal matrix if P = P–1 = PT. An nxn real matrix X is called a generalized centro-symmetric matrix with respect to P, if X = PXP. It is obvious that every nxn matrix is also a generalized centro-symmetric matrix with respect to I (identity matrix). In the present paper, we propose a gradient-based iterative algorithm to solve the generalized coupled Sylvester matrix equations over the generalized centro-symmetric matrix pair (X1,X2). It is proved that the iterative method is always convergent for any initial generalized centro-symmetric matrix pair (X1(1),X2(1)). Finally, a numerical example is discussed to illustrate the results.
The accurate measurement of a geomagnetic field is a key technology in geomagnetic navigation. Many magnetic interferential fields on navigation vehicles, such as ferromagnetic parts and electric equipments, will seriously disturb the magnetometer, and thus should be compensated. This paper develops a new magnetic interferential field compensation method using three-axial magnetometer measurements. The method relies on a formulation derived from the difference of the measured geomagnetic magnitude and its true value. The goal is to evaluate the magnetic interferential field parameters first and then compensate the measurements with them. The trust-region method is adopted to evaluate the parameters in the formulation. A simulation is conducted to test the validity of the method. An experiment is designed and implemented on an underwater vehicle. Both simulation and experimental results indicate that vehicle magnetic interferential field can be well compensated with this method.
This paper proposes a biogeography-based optimization (BBO) method augmented with a Kalman filter, which is called KFBBO, for PID parameter tuning in a wireless networked learning control system (WNLCS). Because of unreliable transmission of data and commands in wireless networks, the control system is noisy and prone to errors, which results in poor performance by the conventional PID method for wireless networked control in real-world applications. BBO as a new evolutionary optimization is proposed to solve this problem by dynamically optimizing the PID control parameters. Because the wireless network environment is noisy, we also use a Kalman filter to counteract the negative effects of noise and to improve the optimization ability of BBO. Simulation experiments are conducted to evaluate our proposed KFBBO, and the results indicate that the control performance obtained by the improved PID method is better than the conventional PID method. Furthermore, this proposed method is applied to a steam turbine power generation system based on a WNLCS, and the results show its feasibility and effectiveness.
Recent works show that the Hilbert curve path for mobile-beacon-based localization in sensor networks has better accuracy than other approaches, such as randomized and circular paths. In this paper, the modified Hilbert curve path is introduced, and its theoretical lower bound error, compared with the basic Hilbert curve path, has been shown using Cramér–Rao lower bound analysis. It is shown that the proposed approach has a more uniform localization accuracy across all the nodes in the environment than the basic Hilbert approach. The simulation results show that this approach, combined with inter-node communication, provides better and close to uniform accuracy compared with previously proposed approaches.
This research presents a method to enhance the accuracy of Kinect’s depth data. The enhanced accuracy is especially useful for triangular mesh reconstruction. Performing such a reconstruction directly from Kinect’s data is erroneous. We show that using a spatial filtering technique can help to reduce such errors considerably. We further apply such a technique for human surface reconstruction in order to assist doctors in a hospital’s burn care unit. Body surface area is estimated by Heron’s formula. Kinect calibration is also documented in this work.
Successful future asteroid landing missions require that the control method provides advanced disturbance rejection performance and strong robustness against parameter uncertainties to give higher accuracy and reliability in the complex space environment. Motivated by the requirement for safe and precise soft landing on asteroids, the finite-time soft-landing problem of an asteroid probe is addressed in this paper via a nonsingular terminal sliding mode (NTSM) control technique. The problem is formulated as a two-point boundary-value constraints control problem, where the initial and terminal requirements of the soft-landing problem are all included in the problem formulations. Then, according to the specific characteristics of the problem, an NTSM control law for soft landing on an asteroid is proposed. Simulation results demonstrate that, compared to the widely used traditional sling mode control method, the proposed method provides a much faster convergence rate, higher accuracies, better disturbance rejection properties and stronger robustness against parameter uncertainties.
This paper is concerned with model predictive control for polytopic systems subject to exogenous disturbance and H2/H performance constraints. It is shown that a better characterization of H2/H performance can be provided by introducing additional free parameters. Meanwhile, input constraints are ensured by using a dilated linear matrix inequality technique, so the utilization of actuator capability is improved. These two aspects bring extra degrees of freedom in optimizing control performance. Recursive feasibility and stability of the controller are proved. Numerical examples verify these properties.
An adaptive regulation approach in linear systems against exogenous inputs consisting of a linear combination of sinusoids with unknown amplitudes, frequencies and phases is proposed within the framework of Youla parameterized stabilizing controllers. The goal of the developed adaptation algorithm is to search, within the set of weighted Ritz-type Youla parameters, for a controller that yields regulation in the closed-loop system. The proposed approach is applied to an active vibration control problem, and the performance of the developed regulator is illustrated by considering the vibration cancellation against exogenous inputs represented as a linear combination of unknown stationary as well as time-varying sinusoidal disturbances.
In this paper, the concept of smart materials has been engaged in order to control and abate the vibrations of non-linear beams. In the meantime, flexural vibration of viscoelastic has been taken into account aimed at reinforcing the carbon nanotube beams. This theory has applied the viscoelastic model to draw out the classical viscoelastic Kelvin–Voigt model. Likewise, the Hamilton’s principle has been employed to derive the non-linear differential equations of beam’s motion as for the piezoelectric patches and also the multiple scales method has been engaged in order to solve the non-linear equation of system motion. A fuzzy controller has been desirably arranged in the piezoelectric actuator/sensor loop to reduce the forced vibrations for any arbitrary stimulation. Due to majestic efficiency of the Bees Algorithm (BA) in the solution of many different engineering problems, it has been engaged for this work. To ensure and confirm the robustness of the proposed approach, three different conditions of forced stimulation have been taken into account for the studied system. In all, the non-linear simulation results unveil the robust performance of the proposed approach based on the BA technique in the damping of the system’s vibrations.
This paper studies the containment control issue of multi-agent systems with time delay. By adopting continuous-time and sampled-data-based control protocols, we establish some necessary and sufficient containment control criteria for multi-agent systems with time delay. For the continuous-time case, our results show that both topology structure and time delay play an important role in the containment control of multi-agent systems, no matter whether the leaders are stationary (first-order system) or dynamic with constant velocity and time-varying position (second-order system). Furthermore, the maximum value of time delay to guarantee the achievement of containment control is given by the Hopf bifurcation theory. Moreover, we extend the results to the sampled-data case. Finally, numerical simulations are given to illustrate our main results.
In this paper, a robust method for the simultaneous coordinated design of the interline power flow controller (IPFC) and power system stabilizer (PSS) based on output feedback controllers in a single-machine infinite-bus power system for the enhancement of power systems low-frequency oscillations damping is presented. The Honey Bee Algorithm, which has a strong ability to find the most optimistic results, is employed to search for optimal IPFC and PSS output feedback controller parameters. A singular value decomposition method is utilized to select the most effective damping control signal of the IPFC output feedback controller. To assess the effectiveness and robustness of the proposed method, simulation studies are carried out for three operating conditions. Analysis of the results shows that the combined design has an excellent capability for damping a power system’s low-frequency oscillations. Moreover, system performance analysis under different operating conditions and performance indices shows the effectiveness of the proposed method even when a severe fault is applied.
High wind power penetration presents many challenges to the flexibility and reliability of power system operation. In this environment, various demand response (DR) programs have received much attention. As an effective measure of DR programs, interruptible load (IL) programs have been widely used around the world. This paper addresses the concern of how the IL program impacts the equilibrium outcomes of electricity markets with wind power. First, a market demand model is developed to take consideration of the IL program. Next, a Cournot equilibrium model for electricity markets with an IL program and wind power is presented. The introduction of the IL program leads to a non-smooth equilibrium problem. To solve this equilibrium problem, a novel solution method is proposed. Finally, considering that wind power penetration will increase the risks faced by the conventional generators, the conditional value at risk is employed to measure the risk, so that the impact of the IL program on the generators’ risk can also be examined. Numerical examples are presented to verify the effectiveness of the method. It is shown that the IL program can lower market price and its volatility significantly. In addition, the IL program can help generators reduce their risks in the market, especially when the uncertainty in wind power output is relatively large.
A binary-type exhaust gas oxygen (BEGO) sensor that is usually used to maintain the air–fuel ratio (AFR) at stoichiometric conditions in current spark ignition (SI) engines is significantly lower in cost compared with the universal exhaust gas oxygen sensor. However, it can only switch from a high state (0.7 V) to a low state (0.1 V) when the mixture goes from richer to leaner than stoichiometric conditions or vice versa. Thus, it cannot indicate the actual AFR. A novel method of estimating the AFR of an SI engine using a BEGO sensor has been demonstrated in this work for leaner than stoichiometric mixtures. Experiments were conducted on a single-cylinder, manifold-injection SI engine. The air–fuel mixture was initially kept at a richer than stoichiometric level while the engine was maintained at constant speed and throttle. The mixture was suddenly changed to a leaner than stoichiometric level. The switching time of the BEGO sensor during this operation was noted. This was repeated for different initial and final AFRs. A relationship between the switching time and change in AFR was obtained for different initial AFRs. A look-up table to determine the AFR was made and used under test conditions. The error is less than 5% in the estimated AFR. The system was also incorporated on a low-cost microcontroller-based engine management system and tested under laboratory conditions.
An underactuated two-dimensional translational oscillator with rotational actuator (2DTORA) consisting of an actuated rotational proof-mass and two unactuated translational carts is presented. Passivity-based control design is employed for 2DTORA based on its Euler–Lagrange structure and passivity property. Firstly, the dynamics of 2DTORA are derived based on Euler–Lagrange equations. Motivated by constructing a damped close-loop Euler–Lagrange system, the controller dynamics is designed to shape the potential energy and inject the required damping. As a result, the designed controller stabilizes the underactuated 2DTORA with the feedback of the rotational actuator’s position only. Moreover, by modifying controller dynamics with a saturation function, the control input can be constrained to certain bounds. Finally, simulation results demonstrate the feasibility and effectiveness of the proposed controllers.
Moving from combustion engine to electric vehicle (EV)-based transport is recognized as having a major role to play in reducing pollution, combating climate change and improving energy security. However, the introduction of EVs poses major challenges for power system operation. With increasing penetration of EVs, uncontrolled coincident charging may overload the grid and substantially increase peak power requirements. Developing smart grid technologies and appropriate charging strategies to support the role out of EVs is therefore a high priority. In this paper, we investigate the effectiveness of distributed additive increase and multiplicative decrease (AIMD) charging algorithms, as proposed by Stüdli et al. in 2012, at mitigating the impact of domestic charging of EVs on low-voltage distribution networks. In particular, a number of enhancements to the basic AIMD implementation are introduced to enable local power system infrastructure and voltage level constraints to be taken into account and to reduce peak power requirements. The enhanced AIMD EV charging strategies are evaluated using power system simulations for a typical low-voltage residential feeder network in Ireland. Results show that by using the proposed AIMD-based smart charging algorithms, 50% EV penetration can be accommodated, compared with only 10% with uncontrolled charging, without exceeding network infrastructure constraints.
The present research attempts to extend the available literature in the area of secure communications to provide an efficient robust adaptive sliding mode control approach in the presence of uncertainties, external disturbances and unknown parameters. It is obvious that the proposed control approach aims to synchronize two hyper-chaotic systems through secure communication. In order to assure the security of signal sending and receiving, the technique of chaotic parameter modulation is taken into consideration. The stability performance of the present approach is accurately guaranteed via the Lyapunov theorem. Numerical simulation results are carried out to illustrate the proposed approach performance, which is easily able to follow the control objectives.
This paper is concerned with the problem of robust reliable control for a class of uncertain 2D discrete switched systems with state delays and actuator faults represented by a model of Roesser type. The parameter uncertainties are assumed to be norm-bounded. Firstly, based on the average dwell time approach, a delay-dependent sufficient condition for the exponential stability of discrete 2D switched systems with state delays is established in terms of linear matrix inequalities. Then, a reliable state feedback controller is designed to guarantee the exponential stability and reliability for the underlying systems. Finally, a numerical example is given to demonstrate the effectiveness of the proposed approach.
Linear matrix equations play an important role in many areas, such as control theory, system theory, stability theory and some other fields of pure and applied mathematics. In the present paper, we consider the generalized coupled Sylvester-transpose and conjugate matrix equations
in which Aviμ, Bviμ, Cviμ, Dviμ, Mviμ, Nviμ, Hviμ, Gviμ and Fv are given matrices with suitable dimensions defined over complex number field. By using the hierarchical identification principle, an iterative algorithm is proposed for solving the above coupled linear matrix equations over the group of reflexive (anti-reflexive) matrices. Meanwhile, sufficient conditions are established which guarantee the convergence of the presented algorithm. Finally, some numerical examples are given to demonstrate the validity of our theoretical results and the efficiency of the algorithm for solving the mentioned coupled linear matrix equations.
Building effective vehicular cyber-physical systems to improve road safety is a non-trivial challenge. The abnormal region of road – ‘road abnormal region’ – is one of the main reasons for traffic congestion and thus should be taken into account in order to inform the design of a road monitoring mechanism and navigation service. The characteristics of the abnormal region (AR) are investigated in this paper and an evaluation criterion is suggested. AnyLogic 6.0 (Education) is the platform used to simulate the generation of AR and its changing procedure, which provides justification for the proposed algorithm.
This paper addresses both dynamic modelling and dynamic parameter identification of a permanent magnet spherical actuator, which is capable of performing three-degree-of-freedom (DOF) motion in one single joint. The dynamic model of the spherical actuator is derived from Lagrange’s equations, but the parameters, called dynamic parameters, in the model are usually uncertain. Then the dynamic model is represented in a form that is linear in these parameters. A new identification method based on the output error (OE) method and recursive least square (LS) estimation is proposed to identify the parameters. This method only requires the current measurement of the stator coils in the identification procedure, which greatly simplifies the experimental process and improves the identification accuracy. Lastly, simulation and experimental results illustrate the effectiveness of the proposed method and its robustness to external disturbances. The proposed method can be also applied to other electromagnetic driving spherical actuators.
This paper investigates the H2 control problem for a class of discrete-time two-dimensional (2D) switched systems represented by a model of Roesser type. By using a multiple Lyapunov function method, sufficient conditions for evaluation of the H2 performance for the 2D switched systems are presented in terms of linear matrix inequalities. Based on the obtained results, an H2 output feedback controller design scheme is developed for the discrete-time 2D switched systems. A numerical example is included to demonstrate the effectiveness of the proposed method.
The overhead crane is an under-actuated system because its degree of freedom is larger than that of actuators. The three state variables of trolley motion, cargo lifting motion and cargo swing are controlled by two input signals composed of trolley driving and cargo lifting forces. In the present study, a novel non-linear control scheme for an overhead crane is proposed based on the combination of two control design techniques. The cargo swing vanishing mechanism is constructed using partial feedback linearization. Control of trolley and cargo tracking is designed based on the sliding mode technique. An anti-swing structure is then merged with the tracking scheme of the trolley and cargo hoisting motions to enable indirect control of the cargo swing angle. Both simulation and experimental results show that the combined controller not only stabilizes all trajectories of system states but also guarantees the robustness in which the shapes of system responses are consistently retained despite the wide variation in crane parameters.
This study involves a layered vehicle dynamics control system, which is composed of an adaptive optimal control allocation method using Lagrangian neural networks for optimal distribution of tyre forces and the sliding mode yaw moment observer for robust control of yaw dynamics. The proposed optimal control allocation method eliminates the requirement of solving optimization problem in every time step and it is a convergent and stability guaranteed solution for the optimal tyre force distribution problem. The aim in the sliding mode yaw moment observer is to force the vehicle to track a reference vehicle dynamic behaviour by estimating the equivalent input extended disturbance, which is the required stabilizing virtual yaw moment. The proposed layered stability control scheme has been tested on a four-wheel drive–four-wheel steer electric Fiat Doblo Van, which is modelled in CarSim. Both the sliding mode disturbance observer and the optimal control allocation methods are the first known applications to the stability control problem of road vehicles.
The highly scalable infrastructure of large-scale distributed systems is very attractive for network services. However, data access is unpredictable in this environment for the reasons of loosely coupled nature and large-scale data storage of such systems. Today, an increasing number of network applications require not only considerations of computation capacity of servers but also accessibility for adequate job allocations. An effective and adaptive mechanism of access control is important in this environment. In our study, the client clustering is used to describe the behaviors of clients and the adaptive server clustering is used to divide the large-scale distributed system into relevant small-scale systems. Since the clients which are assigned to one server cluster have the similar behaviors, we can use the stochastic control and passive measurement to do reliable and adaptive accessibility estimation and client allocation in such a small-scale system. We call this adaptive mechanism of access control based on accessibility estimation and client clustering as ACEC, and the experimental results show that ACEC can significantly reduce the data access cost and guarantee the load balance and controllability of large-scale distributed systems.
This paper aims to present an approach for design of dynamic output feedback compensators for linear discrete-time descriptor systems subject to state and control constraints. To this end, output-feedback controlled-invariant polyhedra are constructed by taking a pair of polyhedral sets: a controlled-invariant set and a conditioned-invariant set. By defining an augmented system composed of the original system plus the dynamic compensator, a control action can be computed online, which optimizes the contraction rate of the augmented state trajectory and enforces the constraints. The results are illustrated through numerical examples, which show that the proposed dynamic compensators outperform static feedback controllers under the same conditions.
In this paper we study the multi-agent saddle-point problems where multiple agents try to collectively optimize a sum of local convex–concave functions, each of which is available to one specific agent in the network. We propose a distributed primal–dual subgradient method under the constraint that agents can only communicate quantized information. The method can be implemented over a time-varying network while satisfying some standard connectivity conditions. We provide convergence results and convergence rate estimates for the proposed method. In particular, we provide an error bound on its convergence rate to highlight the dependence on the quantization resolution. We have also provided a numerical example to show the effectiveness of the proposed method.
The practical ozone dosing process of drinking water treatment is essentially a complicated nonlinear process with time delay. It is difficult to establish an exact mathematical model and implement a satisfying real-time control for the frequent changes of water quality, water flow rate and process operational conditions. In this paper, the control strategy of keeping a constant ozone exposure is attempted instead of conventional keeping a constant ozone dosage or dissolved ozone residual. To this end, an adaptive model predictive control (MPC) scheme based on the radial basis function (RBF) neural network model is proposed to maintain a constant ozone exposure by adjusting ozone dosage. With the proposed control scheme, a RBF neural network model is established as the prediction model of practical ozone dosing process. Then an adaptive model predictive controller is designed. Owing to the online updating of RBF neural-network weights, the proposed MPC scheme can cope with the frequent changes of water quality, water flow rate and process operational conditions. Both simulation and experimental results demonstrate the effectiveness and practicality of this real-time control method.
This paper investigates the identification and output tracking control of a class of Hammerstein systems through a wireless network within an integrated framework and the statistic characteristics of the wireless network are modelled using the inverse Gaussian cumulative distribution function. In the proposed framework, a new networked identification algorithm is proposed to compensate for the influence of the wireless network delays so as to acquire the more precise Hammerstein system model. Then, the identified model together with the model-based approach is used to design an output tracking controller. Mean square stability conditions are given using linear matrix inequalities (LMIs) and the optimal controller gains can be obtained by solving the corresponding optimization problem expressed using LMIs. Illustrative numerical simulation examples are given to demonstrate the effectiveness of our proposed method.
This paper presents some stability synthesis results for the discrete-time linear switching systems whose dynamics contain reset functions and are determined by exogenous and uncontrollable events. First, for autonomous linear switching systems, conditions of stability are given in terms of a new definition of -controlled invariant set, and existence conditions to obtain such a set are presented. Then, under the assumption that the discrete state is known and the continuous state is unavailable for feedback, this new result is used to find the sufficient conditions for the existence of an observer-based stabilizing controller and dynamic output feedback controller. Such a design can be formulated in terms of linear matrix inequalities, which are numerically feasible with commercially available software. Finally, illustrative examples are given to indicate the effectiveness of the proposed design.
In this paper, three fundamental properties of a potential field-based flocking algorithm, i.e. merging of neighbouring graphs during the system evolution, collision avoidance and convergence of position of the centre of mass of informed agents to that of virtual leader are discussed. Next, these properties are utilized to determine required number of informed agents based on initial position of uninformed ones and consequently reduce the domain of search in optimization problems defined for finding the optimal number of required informed agents. Finally, a new optimization framework is proposed, which benefits Voronoi diagrams in order to reduce the number of informed agents required for velocity convergence of the whole group. This optimization framework reduces computational complexity in the cost of lower optimality.
The guidance law design for the missile–target interception problem in the presence of system uncertainty is investigated in this paper. To improve the disturbance rejection property, two kinds of non-smooth guidance laws based on a finite-time control technique are developed. The first is a high-gain finite-time guidance law, which can be derived by assuming that the system uncertainty is bounded by a constant. The second one is a composite guidance law including a disturbance observer and a finite-time state feedback, where the disturbance observer is introduced to estimate the system uncertainty and the finite-time state feedback is to stabilize the guidance system. Under both of the proposed guidance laws, it can be proved that the line-of-sight angle rate will converge to the origin or a small region of the origin in a finite time. Simulation comparisons show the effectiveness of the proposed method.
In this paper, an adaptive fuzzy-sliding control system is proposed to improve the dynamic performance of a three-phase active power filter (APF). Adaptive fuzzy controllers are employed to approximate both the equivalent control term and the switching control term in the sliding mode controller. An online adaptive tuning algorithm for the consequent parameters in the fuzzy rules is also designed. The switching control becomes continuous and the chattering phenomena can be attenuated. Simulation demonstrated that the proposed control method has an excellent dynamic performance such as small current tracking error, reduced total harmonic distortion (THD), strong robustness in the presence of parameter variation and non-linear load.
The primary task of the electronic pressure (E/P) valves is to control the pressure continuously in different industrial automation processes. Their use in pneumatic drives allows regulating pressures in both cylinder chambers and so it is possible to achieve a direct force controlled actuator. Additionally to the force control, position control of the pneumatic actuator using E/P valves has been presented. A dynamic model for control purposes of the process has been derived, followed by the PID controller tuned according to damping optimum criteria. The control algorithms have been experimentally verified on an industrial cylindrical rod-less actuator controlled by two E/P valves. The control performances are comparable with the set-up with proportional directional control valves (servo-valves). Based on the experimental results, it can be concluded that these valves have potential for successful implementation in industry for position and force control applications.
Environment exploration and recognition play an important role for service robots to operate in indoor environments that have a large scale and dynamic changes. This paper presents a new geometric model of the environment that is constructed with line segments and ellipses by estimating geometric properties and extracting geometric discontinuities. Two new types of indoor environment representations, namely exploration direction point and topological opening point (TOP), are defined using this model for autonomous exploration and spatial scene segmentation. The environment marks at TOPs are extracted from 2D laser data and a novel environment recognition algorithm is developed to accomplish environment recognition tasks autonomously. The experimental results show that the proposed approach can effectively explore and recognize a large-scale indoor environment in real time.
In this paper, a feedforward-plus-sliding mode controller is proposed. The combination uses a feedforward controller to achieve predefined process output and the sliding mode controller is combined to assure the robustness despite uncertainty, nonlinear dynamics, and external disturbances. The effectiveness of proposed method is shown by simulations on nonlinear models of a coupled tanks system and then validated by performing a real-time experiment on the system. The studies show the improved setpoint tracking of the proposed controller compared with the classical PI controller.
We focus on the issue of outlier detection for time-series data in a process control system (PCS), since outlier detection is a critical step before performing data-based system analysis. Several published articles have proved that a wavelet transform (WT) technique can be used to detect outliers in time-series data, but the standard WT detection method, as well as any other univariate outlier detection technique, does not distinguish between the sudden change caused by the changes of inputs and the fluctuations caused by outliers in PCS. In order to improve this shortcoming of the conventional WT method for the data in a PCS, a new algorithm combining the wavelet technique with a robust radial basis function (RBF) network is proposed here. In this method, a robust RBF network (RBFN) training algorithm is proposed, which can train the RBFN online using the original data as a training set without the need of clean data and thus fits the application of online detection. Furthermore, a hidden Markov model is adopted as an analysis tool to accomplish online automatic detection without pre-selecting the threshold. We compare the performance of our proposed method with the conventional wavelet method and the AR model method to demonstrate its validity through simulation and experimental applications to the data pretreatment process in an electric arc furnace electrode regulator system.
Two adaptive principal component analysis methods are improved based on adaptive extracting principal components (PCs) in process monitoring: recursive PCA (RPCA) and moving window PCA (MWPCA). An adaptive extracting PC algorithm is proposed using the threshold method based on the score rule in sport games to determine the number of PCs in real time. It can effectively overcome the shortcomings of the conventional cumulative percent variance method in obtaining the number of PCs. Moreover, two improved RPCA and MWPCA methods are proposed using the new threshold method to monitor an industrial process online. Similary to the forgetting factor in RPCA, an optimal variable moving window size is selected, adding forgetting factors into the data samples and covariance matrices, respectively. The results show the validity of improvements compared with the original RPCA and MWPCA in Tennessee Eastman process monitoring.
This paper is concerned with an optimal distributed control problem of a nonlinear viscous dispersive wave equation that approximately describes the unidirectional propagation of long waves. By the Dubovitskii and Milyutin functional analytical approach, we prove the Pontryagin maximum principle of the investigational system. The necessary condition for optimality is established for the controlled object in a fixed final horizon case and, subsequently, a remark on the applicability of the obtained results is made for the illustration.
This paper considers the problem of stabilizing a first-order plants with known time delay using a fractional-order proportional–integral controller PI. Using a generalization of the Hermite–Biehler theorem applicable to quasi-polynomials, a complete analytical characterization of all stabilizing gain values (kp, ki) is provided. The widespread industrial use of fractional PI controllers justifies a timely interest in PI tuning techniques.
In the practical control of microelectromechanical system (MEMS) gyroscopes, dead-zone non-linearity often exists, which has negative influence on the resolution and performance of the gyroscope system. System non-linearities are inevitable in actual MEMS gyroscopes and require the controller to be either adaptive or robust to these non-linearities. In this paper, an adaptive fuzzy compensator is designed to compensate the dead-zone non-linearities for MEMS gyroscope. A fuzzy logic system is used for dead-zone non-linear switching function and an optimal algorithm is designed to make the dead-zone compensator to tune the parameters adaptively. The closed-loop stability can be guaranteed with the proposed adaptive fuzzy dead-zone compensation. Simulation results demonstrate that the tracking error can be attenuated efficiently and robustness of the control system can be improved with the proposed adaptive fuzzy non-linearity compensator.
Non-linearities commonly exist in an electro-hydraulic servo shaking table, causing acceleration harmonics distortion when the shaking table is excited by a sinusoidal acceleration signal, because its acceleration response includes higher harmonics, which lower the control performance for an electro-hydraulic servo shaking table. To cancel the harmonics in the system response, thus to improve the shaking table performance, we need to know about the harmonics information. An identification algorithm is developed here based on a Kalman filter for dynamically tracking the acceleration harmonics for the electro-hydraulic servo shaking table. A linear system in state space is modelled. The system acceleration response is applied as an observation value and is imported to the Kalman filter, which recursively estimates the state vector of the linear system. The amplitude and phase of each harmonic are calculated from the estimated state vector, and their estimated values are validated. A simulation example is presented and experiments were performed on the electro-hydraulic servo shaking table. Both results show a good estimation performance of the proposed acceleration harmonic identification algorithm.
The gap sensor plays an important role for a electro-magnetic levitation system, which is a critical component of high-speed maglev trains. An artificial neural network is a promising area in the development of intelligent sensors. In this paper, a radial basis function (RBF) neural network modelling approach is introduced for the compensation of the non-contact inductive gap sensor of the high-speed maglev train. As an inverse model compensator, the designed RBF-based model is connected in series to the output terminal of the gap sensor. The network is trained by using a gradient descent learning algorithm with momentum. This scheme could estimate accurately the correct air-gap distance in a wide range of temperatures. The simulation studies of this model show that it can provide a compensated gap value with an error of less than ±0.4 mm at any temperature from 20° to 80°C. In particulr, the maximum estimation error can be reduced to ±0.1 mm when the working gap varies from 8 to 12 mm. The experimental results indicate that the compensated gap signal could meet the requirements of the levitation control system.
Chinese sign language has been proved as an effective communication tool for deaf people. In this paper, we present a novel translation system, which can capture human gestures through micro-inertial measurement units (IMUs) and translate the gestures into specific meanings accordingly. Each micro-IMU consists of a 3D accelerometer and gyroscope. A micro-controller and ZigBee network were used to acquire data simultaneously and wirelessly. Ten types of basic Chinese sign language movements including what, how, work, today, happy, please, book, body, clothes and and were collected and stored to form a motion-sensing database. A discrete cosine transform (DCT) was performed to extract the effective features from the original data, while a hidden Markov model (HMM) was used to train the database in order to form an HMM classifier. Testing samples were used to test the HMM classifier. Different sign languages were recognized through the HMM classifier and subsequent translation processes were performed. Experimental results showed that the correct recognition rate ranges from 95% to 100% for the 10 sign language movements, and the overall correction rate is 98%. With more micro-electro-mechanical system (MEMS) sensing motes adding to the interpretation system, the performance will be enhanced.
We compare the state estimation performance of various nonlinear filters using experimental data. The experiment, a mobile robot driving on a planar surface, provides noisy odometry and laser rangefinder measurements, while groundtruth is provided by an accurate motion capture system. We investigate the localization accuracy of standard extended Kalman and sigma point filters, and compare their performance with adaptive extended Kalman and adaptive sigma point filters. The adaptive filters update the noise covariance matrices based on the measurements available at a given time step (without using groundtruth data). The groundtruth data is used to assess the performance of each filter. Our results show that the adaptive schemes outperform the equivalent traditional formulations, however, they are slightly more difficult to implement and tune.
Solving linear matrix equations has various applications in control theory, in engineering, in scientific computations and various other fields. By applying generalization of the Hermitian and skew-Hermitian splitting (GHSS) iteration and the hierarchical identification principle, we propose a gradient-based iterative method for finding the solution of the generalized coupled Sylvester matrix equations (including (coupled) Sylvester and Lyapunov matrix equations as special cases). We prove that the iterative solution consistently converges to the solution for any initial matrix. Some numerical examples and applications are provided to illustrate the effectiveness of the method.
This paper focuses on the automatic generation of forward kinematics of a kind of modular reconfigurable robot. Based on the modularized division of robots and the communication between the host and the modules, a configuration recognition method is proposed. By using the graph theory, the method of topological analysis is proposed, and the assembly incidence matrix (AIM) and path matrix are derived. Subsequently, based on the results of topological analysis and the definition of module frames, the initial poses and twists of a robot are obtained. To deal with the multi-chain robots, the entries of the path matrix are employed to enable dyads to appear or not to appear in the kinematic equations. Then, the forward kinematics of the multi-chain robot is derived. An illustrative example and an experiment are presented. The results show that the method is valid and suitable for both single-open-chain robots and multi-chain robots.
This paper presents a systematic trajectory generation method for a bipedal robot walking on slopes. A suitable offline walking pattern based on an inverted pendulum model is designed and the planning is parameterized by step lengths, step period and other walking parameters. Meanwhile, a slight unevenness of a slope can cause serious instability for bipedal walking robots. Therefore, this paper also proposes an online control algorithm for a bipedal robot even walking on the unevenness slope, and the robot can adapt to the floor conditions. The control algorithm includes a landing time controller, landing direction controller, zero moment point (ZMP) regulation controller and attitude correction controller. During the process, accurate attitude information for these controllers is achieved through an adaptive filtering method and the ZMP position is measured through force sensing register sensors, which are attached to the robot’s feet. Finally, the experiment is carried out on a SCUT-I humanoid robot. The result proves that the method described in this paper can successfully control a robot walking on slopes.
A new algorithm named the likelihood-based iteration square-root cubature Kalman filter (LISRCKF) is provided in this study. The LISRCKF inherits the virtues of the square-root cubature Kalman filter (SRCKF), which uses the cubature rule-based numerical integration method to calculate the mean and square root of covariance for the non-linear random function. The LISRCKF involves the use of the iterative measurement update and the use of the latest measurement, and the iteration termination criterion based on maximum likelihood is introduced in the measurement update. The LISRCKF algorithm is applied to the state estimation for re-entry ballistic target with unknown ballistic coefficient. Its performance is compared against that of the unscented Kalman filter and SRCKF. Moreover, the suitable choice of iteration number is studied; iteration number 5 is the most appropriate for the LISRCKF algorithm. Simulation results indicate that the LISRCKF algorithm has the features of short run time and fast convergence rate; the advantage in robustness is also demonstrated through the numerical simulation, and it is an effective state estimation method.
Human upright postural control is highly related to visual information. In order to explore the influence of visual feedback on static upright postural control, postural sway of eight healthy young adults was investigated under visual feedback circumstances. In the investigation, postural feedback information was visualized by an indicator composed of a movable spot and a stationary circle, and in addition to Shannon entropy analysis, time domain and frequency domain analyses were employed to inspect postural control adjustment. The experiment results indicate that even though indicator scale changes do not induce significant postural differences in time domain and random characteristics, reduction of the visual feedback indicator scale inspires a postural power shift to higher frequencies. In addition, this reduction also induces a fall-after-rise pattern of postural energy distribution in the frequency range of 0.5–1 Hz.
A new general 3D object model is required in the literature of smart camera networks to facilitate future research. This paper presents a novel hierarchical and structural 3D model description which is well suited for both events detection and real-time free viewpoint surveillance. With this 3D model, sparse points are used to reconstruct objects. In addition, the state of the model is easy to track and estimate, which can be used to reduce time and computation when reconstructing the model. Further, data flow in the network is reduced to a level that smart cameras can afford. A concrete data structure of the model is described in this paper and its reconstruction method, the fusion method, is provided. Finally, experiment results show its feasibility, efficiency and effectiveness.
This paper describes a microfluidic-based telemedicine system for insulin detection and conveying the results digitally to physicians located off-site through the Internet. The communication infrastructure is designed to transfer the digital information from the assay site to established healthcare facilities where trained medical professionals can directly assist the detection process and provide diagnosis. The insulin detection device of the telemedicine system is an integrated polydimethysiloxane (PDMS) microfluidic device consisting of two pneumatic micropumps and one micromixer. The insulin detection protocol is based on microbeads-based double-antibody sandwich immunoassay coupled with luminal–hydrogen peroxide (H2O2) chemiluminescence. A photometer detects the peak value of the luminous intensity, which indicates the insulin concentration of the patient plasma sample tested. The calibration curves of the insulin detection protocol have been quantified. The insulin detection limit of the microfluidic system is 4x10–10 mol/l, which meets the common requirement of the current clinical studies of diabetes. Multiple immune indicators of diabetes can potentially be detected synchronously by the microfluidic system, thus providing physicians with integrative results necessary for accurate diagnosis via the Internet. The combination of microfluidic devices and telemedicine strategy offers new opportunities for diabetes care and screening, especially in rural areas where patients must travel long distances to physicians for healthcare information that might be obtained more cost effectively by local, less-trained personnel.
A vehicle with four powered caster wheels can provide much more motion flexibility in a constrained environment. However, the dynamic modelling and control of such system is challenging due to its high redundancy. This paper investigates the dynamic model and tracking control for a four-powered-caster vehicle (4-PCV) on complex terrain without any additional sensor. The torques applied to the wheels are dynamically redistributed based on the real-time conditions of the whole wheel–ground interactions so that the vehicle can track the desired trajectory when moving on different terrains. A dynamic model considering the wheel–ground interaction is first derived. Then a novel approach based on a probability scheme is proposed to identify the status of the vehicle and the wheel slip ratio by only observing the velocity feedback from motors encoders. Based on this real-time perception information, a tracking controller and a torque distribution scheme are applied to make sure that each wheel can be self-adapted to meet a complex wheel–ground condition to eliminate or reduce the probability of slippage. The effectiveness of the proposed estimation approach and the performance of the torque distribution schemes are verified by simulation.
Outlier detection plays an important role in intelligent cyber systems, especially for fault-tolerant and adaptive ones. Traditional algorithms always need to evaluate distances or densities, which are very time-consuming. Considering the increasingly urgent demand for real-time application, during the past few years, various novel algorithms have been proposed. They are much faster, but less stable and less accurate. To cope with these problems, based on the core idea of ordinal optimization and the ‘few and different’ characteristics of outliers, by introducing the concept of outlier probability, we propose this ordinal outlier detection algorithm (OOD), which extracts outliers in terms of the order of being isolated in a recursive uniform data space partitioning process. It does not need any distance or density evaluation, and the complexity is reduced to O(n). Experiments show that the CPU time of OOD increases linearly with linearly growing data sets. Furthermore, compared with the recent iForest algorithm, OOD is about 30 times faster, with a 20–30% improvement in accuracy and in particular is much more stable. OOD also has good scalability, so it works well in high-dimensional data sets, which have a huge number of instances and irrelevant attributes.