Nonlinear optimal control for flight vehicles using neural operators and physics-informed neural networks
Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering
Published online on May 26, 2026
Abstract
Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering, Ahead of Print.
This study introduces a framework for computing optimal control inputs by integrating physics-informed deep learning with classical optimal control theory. The governing differential equations and terminal constraints are formulated via the Euler-Lagrange ...
This study introduces a framework for computing optimal control inputs by integrating physics-informed deep learning with classical optimal control theory. The governing differential equations and terminal constraints are formulated via the Euler-Lagrange ...