Adaptive Dynamic Surface Control of Nonlinear Switched Systems with Prescribed Performance

Abstract

This paper studies the adaptive control problem for a class of switched nonlinear systems with guaranteed prescribed performance bounds under arbitrary switchings. The unknown system functions are approximated by radial basis function neural networks. We employ dynamic surface control technique to solve the explosion of the complexity problem. We control the transformed system such that it is stabilizable, by this way, the original system is stabilizable as well as the prescribed performance is guaranteed. The proposed controller and update laws guarantee the stability of the closed-loop system and the boundedness of all signals. Furthermore, the prescribed performance is ensured. Finally, a simulation example is employed to illustrate the effectiveness of the proposed method.