Adaptive neural tracking control for a class of uncertain switched nonlinear systems with unknown backlash-like hysteresis control input

This paper is concerned with the problem of adaptive tracking control for a class of uncertain switched nonlinear systems. Completely unknown backlash-like hysteresis control input that frequently exists in practice is also considered. By combining adaptive backstepping technique with neural networks approximation ability, an adaptive neural control algorithm is presented for the systems under consideration. A common virtual control function is deigned to construct a common Lyapunov function for the system. The explosion of complexity in traditional backstepping design is avoided by using dynamic surface control. It is demonstrated that the practical output tracking performance is achieved by using the proposed state-feedback controllers, and all the signals remain bounded. Finally, simulation results are given to show the effectiveness of the theoretical approaches.