Constrained adaptive neural control for a class of nonstrict-feedback nonlinear systems with disturbances

Abstract

This paper focuses on the tracking problem for a class of nonstrict-feedback nonlinear systems with mismatched unknown nonlinear functions and external disturbances. First, a disturbance observer is developed to estimate the disturbance generated by an exogenous system. Then, based on the output of the disturbance observer, a constrained adaptive neural controller is developed for the nonstrict-feedback nonlinear system. In the control scheme design, the modified variable separation approach is applied to establish the relationship between the bounded function of nonstrict-feedback nonlinear function and the error variable. Furthermore, the barrier Lyapunov function is applied to guarantee that full state constraints are not violated. As a result, all the signals of the closed-loop system are semi-global uniformly ultimately bounded. Finally, two simulation examples are used to demonstrate the effectiveness of the developed constrained adaptive neural control law.