Zero-Sum Game-Based Practical Predefined-Time Reinforcement Learning for Robust Tracking Control of Highly Flexible Aircraft

Abstract

This paper develops a zero-sum game-based practical predefined-time reinforcement learning method for robust tracking control of highly flexible aircraft. First, the disturbed tracking problem of highly flexible aircraft is transformed into a min–max optimal control problem, where the controller minimizes the infinite-horizon performance index and the adversarial disturbance maximizes it. Then, a critic neural network is used to approximate the solution of the Hamilton–Jacobi–Isaacs equation online, and a fractional-power critic-update law is constructed so that the critic weight estimation error converges to a bounded neighborhood within a user-predefined time. The boundedness of closed-loop and practical predefined-time convergence of critic weight estimation error are rigorously proven. Several simulations are conducted to verify the advancement, effectiveness and robustness of this control algorithm. Overall, the results demonstrate that the proposed method provides an effective practical predefined-time learning framework for robust tracking control of highly flexible aircraft.