Adaptive neural control for switched nonlinear systems with unknown backlash-like hysteresis and output dead-zone

Abstract

This paper investigates the problem of adaptive neural tracking control for a class of uncertain switched nonlinear systems in nonstrict-feedback structure. It should be pointed out that both the issues of unknown backlash-like hysteresis and output dead-zone are involved in the considered systems. A Nussbaum-type gain function is presented in this paper to overcome the difficulty existing in tracking the output dead-zone with unknown control direction, and the variable separation technique is applied to decompose the unknown functions containing all state variables into the sum of smooth functions with each error dynamic. Then by combining the backstepping approach and the approximation capability of radial basis function neural networks, an adaptive neural tracking control strategy is constructed, which guarantees that all the signals of the closed-loop systems are controlled to be semi-globally uniformly ultimately bound and the tracking error converges to a small region around the origin. Finally, the effectiveness of the proposed control strategy is verified by two simulation examples.