Adaptive Neural-Network Tracking Stabilization for Switched Nonlinear Systems with Disturbances

摘要：

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.

关键词： RBF neutral networks H∞ control Asymptotical stability Disturbance attenuation

作者: Lei Yu Shumin Fei

作者单位: School of Mechanical and Electrical Engineering, Soochow University, Suzhou, 215021, China;School of School of Automation, Southeast University, Nanjing,210096, China

会议类型: 国际会议

会议名称: 2011 China Control and Decision Conference(2011中国控制与决策会议 CCDC)

会议地点: 四川绵阳

会议语种:英文

页码: 1391-1394

在线出版日期: 2011-05-23（万方平台首次上网日期，不代表论文的发表时间）