Neural Learning Control for Discrete-Time Strict-Feedback Systems: An Error Estimate Method

Abstract

This paper addresses the issue of neural learning control for a kind of discrete-time strict-feedback system. Firstly, by using an error dynamics estimator, a new delay-free NN update law is proposed. Subsequently, after making the system output tracks a given recurrent tracking signal, the estimated NN weights can be demonstrated to converge to a small area of their ideal values, exponentially, which can be stored as constant NN weights. Then, a neural learning controller is proposed by using the constant NN weights and disturbance observer. Compared with the previous neural learning controller, the proposed scheme can not only avoid the chattering problem that may be caused by controller switching but also enhance the system robustness by implanting the disturbance observer.