Robust adaptive control for a class of switched nonlinear systems in pure-feedback form

Abstract

An adaptive neural network control scheme is proposed for a class of nonlinear switched systems in pure-feedback form. The design is based on the dynamic surface technique, the approximation capability of neural networks and the dwell-time approach. The design makes the approach of dynamic surface control being extended to the switched nonlinear system, and relaxes the extent of application of the approach of dynamic surface control. Compared with existing literatures, the proposed approach relaxes the requirements of the system. And the explosion of complexity in traditional backstepping design caused by repeated differentiations of virtual control is avoided. By theoretical analysis, the closed-loop control system is shown to be semi-globally uniformly ultimately bounded. Finally, simulation results are presented to illustrate the effectiveness of the proposed approach.