Adaptive neural output feedback control for a class of switched non

This paper investigates the problem of adaptive neural output feedback control for a class of switched non-linear systems, and the unknown backlash-like hysteresis of the actuator is also taken into consideration. First, neural networks are used to approximate the uncertain functions in the studied system. Second, a state-observer is proposed to estimate the system states. Finally, an adaptive neural output feedback control algorithm based on a backstepping technique is constructed; in addition, dynamic surface control is applied to eliminate the explosion in complexity caused by the backstepping technique. By using Lyapunov stability theory, it is proved that all the signals of the switched system are bounded under the proposed control scheme. The effectiveness of the proposed approach is further confirmed by simulation experiments