Adaptive neural control of nonstrict system with output constriant

Abstract

This paper focuses on adaptive neural control for nonlinear system in nonstrict feedback form in the presence of output constraint. Since the backstepping control can not be directly employed to nonstrict feedback structure during controller design. Using the variable separation method, the above obstacle has been overcome. Then, by utilizing barrier Lyapunov function, the issue of output constraint is handled. Combing neural networks (NNs) with the adaptive backstepping technique, it is not only guaranteed that all variables remain bounded in the closed-loop system, but the tracking error is made around the zero with an adjustable small neighborhood. A numerical simulation is provided to demonstrate the control scheme.