Prescribed Performance Optimal Backstepping Control for Hypersonic Vehicles Based on Reinforcement Learning With Disturbance Observer

This article presents a prescribed performance optimal backstepping control method based on reinforcement learning (RL) to address the challenge of achieving optimal control for hypersonic vehicles (HSVs) while accounting for dynamic performance under disturbances. A nonlinear model of the HSV is developed, and the controller design is divided into altitude and velocity subsystems. Optimal control commands for each subsystem are derived using RL within the actor–critic framework. To enhance the antidisturbance capabilities of RL, the total system uncertainties are estimated through an extended state observer (ESO), effectively balancing optimal control with disturbance rejection. The proposed lightweight RL method incorporates an adaptive update law for weight adjustment, eliminating the need for the trial-and-error process typical of conventional RL. Furthermore, a time-varying barrier Lyapunov functions based on prescribed performance theory ensures the stability of the closed-loop system and ensures global state convergence within prescribed performance bounds. Simulation results confirm the effectiveness and superiority of the proposed method.