Prescribed Performance Optimal Tracking Control for Nonlinear Systems

Zongsheng Huang

Abstract: In this paper, based on reinforcement learning (RL) technique, the optimal tracking control problem is considered for a class of strict-feedback nonlinear systems with prescribed performance (PP). The radial-basis-function (RBF) neural network (NN) is introduced to identify the unknown nonlinearities. Depending on the PP technique, the tracking error can be limited in the prescribed area. To guarantee the output of the system tracking the reference signal synchronously in an optimal way in strict-feedback nonlinear system, the adaptive backstepping control scheme is firstly established. Subsequently, the optimal controller is derived via policy iteration. Therefore, the whole controller consists of the adaptive controller and optimal controller. The stability analysis shows that all signals in the closed-loop system are bounded. The effectiveness and advantages of the designed control strategy are verified by the simulation examples.

Submission Number: 225