Adaptive neural tracking control of stochastic nonaffine nonlinear switched systems with unknown backlash-like hysteresis

Abstract

This paper is concerned with the tracking control problem for a class of switched nonaffine stochastic nonlinear systems in completely nonaffine form and nonlower-triangular structure, with unknown backlash-like hysteresis involved, and a novel adaptive neural tracking control scheme, based on backstepping design, is proposed. To eliminate the problem of complexity explosion, dynamic surface control (DSC) technique is incorporated into the backstepping design procedure, such that the process of controller design becomes much simpler. High-order neural networks (HONNs) are employed to approximate the lumped unknown nonlinear functions, and only one adaptive parameter is required to be updated. Stability analysis shows that the proposed scheme guarantees all the closed-loop error signals are semi-globally uniformly ultimately bounded in the 4th-moment or mean square, and the system output can converge to an arbitrary small neighbourhood of the given trajectory. Finally, simulation results are presented to verify the effectiveness of the proposed approach.