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

作     者：Lei Yu is with the School of Mechanical and Electrical Engineering,Soochow University,Suzhou,215021,China,and with the School of Automation,Southeast University,Nanjing,210096,China.Shumin Fei is with the School of Automation,Southeast University,Nanjing,210096,China. 

会议名称：《2011 Chinese Control and Decision Conference（CCDC）》

会议日期：2011年

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 08[工学] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：supported by the Key Program of National Natural Science Foundation of China under Grant 60835001 

关 键 词：RBF neutral networks H∞ control Asymptotical stability Disturbance attenuation 

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 *** neural networks （NNs） are used to approximate unknown functions for solving the restraints of feedback linearizable *** 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 *** proposed control scheme can guarantee asymptotical stability and disturbance attenuation performance of tracking error for switched nonlinear systems under all admissible switching ***,we give a simulation example to illustrate the effectiveness of the proposed control scheme.