Adaptive reinforcement learning optimal tracking control for strict-feedback nonlinear systems with prescribed performance

The reinforcement learning-based prescribed performance optimal tracking control prob-lem is considered for a class of strict-feedback nonlinear systems in this paper. The unknown nonlinearities and cost function are approximated by radial-basis-function (RBF) neural network (NN). The overall controller consists of an adaptive controller and an optimal compensation term. Firstly, the adaptive controller is designed by backstepping control method. Subsequently, the optimal compensation term is derived via policy itera-tion by minimizing cost function. In addition, depending on the prescribed performance control, the tracking error can be limited in the prescribed area. Therefore, the whole con-trol scheme can effectively guarantee that the tracking error converges to a bound with prescribed performance while the cost function is minimized. The stability analysis shows that all signals in the closed-loop system are bounded. Finally, the effectiveness and advan-tages of the designed control strategy are illustrated by the simulation examples.(c) 2022 Elsevier Inc. All rights reserved.