Neural-networked adaptive tracking control for switched nonlinear pure-feedback systems under arbitrary switching

Abstract

This paper deals with the problem of adaptive tracking control for a class of switched uncertain nonlinear systems in pure-feedback form under arbitrary switching. Based on command filtered backstepping design and common Lyapunov function method, a robust adaptive neural-networked control scheme is proposed to guarantee that the resulting closed-loop system is asymptotically bounded with tracking error converging to a neighborhood of the origin. A universal formula for constructing common neural-networked stabilizing function and controller is designed. Differing from the existing results in the literature, the developed new design scheme only requires desired trajectory and common stabilizing functions/virtual control signals instead of them and their first derivatives at each step in backstepping design procedures, and does not need a priori knowledge of the signs of control gain functions. Simulation results illustrate the effectiveness of the proposed techniques.