Adaptive output-feedback neural tracking control for a class of nonstrict-feedback nonlinear systems

Abstract

This paper focuses on the problem of adaptive neural output-feedback control for a class of nonstrict-feedback nonlinear systems where the system coefficient functions are unknown. First, the original system is transformed into a new defined system by a linear state transformation. Then, by using the dynamic surface control (DSC) technique, an improved input-driven filter is proposed. Based on this filter and the approximation property of radial basis function (RBF) neural networks, an adaptive neural output-feedback controller is designed via backstepping technique, which can guarantee that all the signals in the closed-loop system are ultimately bounded. The main contribution of this paper lies in that a simpler and more effective design procedure of adaptive neural output-feedback tracking controller is proposed for the underlying system which is more general than some existing ones in literature. Finally, simulation results are given to demonstrate the feasibility and effectiveness of the new design algorithm.