Adaptive neural tracking control for a class of switched uncertain nonlinear systems

This paper investigates the problem of adaptive neural tracking control for a class of switched uncertain nonlinear systems in nonstrict-feedback form. By resorting to the adaptive backstepping technique, common Lyapunov function method, variable separation approach and the universal approximation capability of radial basis function neural networks, an adaptive tracking control algorithm is proposed for the considered system. It is shown that the designed controller can ensure that the target signal can be tracked with a small bounded error and the stability of the system can be kept under arbitrary switchings. Finally, simulation studies for a ship maneuvering system under different speeds are presented to show the effectiveness of the proposed method.