Adaptive neural-network tracking stabilization for switched nonlinear systems with disturbances

In this paper, the robust adaptive tracking stabilization problem in the sense of uniformly ultimate boundedness (UUB) for a class of switched nonlinear systems with external disturbances is developed. RBF neural networks (NNs) are used to approximate unknown functions for solving the restraints of feedback linearizable techniques. The weights of RBF NNs updated laws and switching signals have been derived to make the closed loop system Lyapunov stable. A robust H_∞ controller is designed to enhance robustness due to the existence of the compound disturbance which consists of approximation errors of the neural networks and external disturbance. The proposed control scheme can guarantee asymptotical stability and disturbance attenuation performance of tracking error for switched nonlinear systems under all admissible switching strategy. Finally, we give a simulation example to illustrate the effectiveness of the proposed control scheme.