ADP-Based Attitude-Tracking Control with Prescribed Performance for Hypersonic Vehicles
Shangwei Zhao, Jingcheng Wang, Haotian Xu, Bohui Wang
Abstract
This paper proposes an observer-based adaptive dynamic programming (ADP) approach to handle the optimal attitude-tracking control tasks for hypersonic vehicles with prescribed performance constraints. Herein, the biggest challenge is how to design an optimal attitude-tracking controller which could balance performance constraints and optimization capabilities with the presence of partially unknown dynamics and actuator saturation. To this end, the transformation function is designed to convert the constrained tracking error into the equivalent unconstrained one. After that, a constraint-aware neural network-based observer is proposed to estimate the future tracking error information required by the optimal controller, which avoids the requirement of the prior nonlinear dynamics knowledge, and its estimation errors are compensated through online feedback. Taking the estimated future attitude-tracking error into account, the critic-only ADP approach is designed via the value iteration to achieve the near-optimal control strategy for equivalent systems with a simplified optimized control structure. Furthermore, a novel experience replay-driven weight updating law is provided to circumvent the conventional persistent excitation condition with an online-verification condition, and the algorithm convergence and closed-loop stability are discussed with the help of the Lyapunov technique and induction. Finally, a comprehensive simulation of an attitude-tracking control system for hypersonic vehicles is provided to verify the effectiveness and superiority of the proposed approach.