Reinforcement learning based optimized backstepping control for hypersonic vehicles with disturbance observer

This paper introduces an optimized backstepping control method for Flexible Air-breathing Hypersonic Vehicles (FAHVs). The approach incorporates nonlinear disturbance observation and reinforcement learning to address complex control challenges. The Minimal Learning Parameter (MLP) technique is applied to manage unknown nonlinear dynamics, significantly reducing the computational load usually associated with Neural Network (NN) weight updates. To improve the control system robustness, an MLP-based nonlinear disturbance observer is designed, which estimates lumped disturbances, including flexibility effects, model uncertainties, and external disruptions within the FAHVs. In parallel, the control strategy integrates reinforcement learning using an MLP-based actor-critic framework within the backstepping design to achieve both optimality and robustness. The actor performs control actions, while the critic assesses the optimal performance index function. To minimize this index function, an adaptive gradient descent method constructs both the actor and critic. Lyapunov analysis is employed to demonstrate that all signals in the closed-loop system are semiglobally uniformly ultimately bounded. Simulation results confirm that the proposed control strategy delivers high control performance, marked by improved accuracy and reduced energy consumption.