Adaptive tracking H-infinity control for switched nonlinear systems with unknown control gain sign  

作　　者：LeiYU ShuminFEI HairongZHU XunLI 

机构地区：[1]School of Mechanical and Electrical Engineering,Soochow University,Suzhou Jiangsu 215021,China [2]SchoolofAutomation,SoutheastUni＇Jersity,NanjingJiangsu210096,China

出　　处：《控制理论与应用（英文版）》2011年第4期579-583,共5页

基　　金：supported by the National Natural Science Foundation of China (Nos. 60835001, 60804017);the Application Research Programs of Nantong City ( No. K2010057);the Open Project from digital manufacture technology Key Laboratory of Jiangsu Province (No. HGDML-0908)

摘　　要：This paper addresses the adaptive tracking control scheme for switched nonlinear systems with unknown control gain sign. The approach relaxes the hypothesis that the upper bound of function control gain is known constant and the bounds of external disturbance and approximation errors of neural＇networks are known. RBF neural networks （NNs） are used to approximate unknown functions and an H-infinity controller is introduced to enhance robustness. The adaptive updating laws and the admissible switching signals have been derived from switched multiple Lyapunov function method. It＇s proved that the resulting closed loop system is asymptotically Lyapunov stable such that the output tracking error performance and H-infinity disturbance attenuation level are well obtained. Finally, a simulation example of Forced Duffing systems is given to illustrate the effectiveness of the proposed control scheme and improve significantly the transient performance.This paper addresses the adaptive tracking control scheme for switched nonlinear systems with unknown control gain sign. The approach relaxes the hypothesis that the upper bound of function control gain is known constant and the bounds of external disturbance and approximation errors of neural＇networks are known. RBF neural networks （NNs） are used to approximate unknown functions and an H-infinity controller is introduced to enhance robustness. The adaptive updating laws and the admissible switching signals have been derived from switched multiple Lyapunov function method. It＇s proved that the resulting closed loop system is asymptotically Lyapunov stable such that the output tracking error performance and H-infinity disturbance attenuation level are well obtained. Finally, a simulation example of Forced Duffing systems is given to illustrate the effectiveness of the proposed control scheme and improve significantly the transient performance.

关 键 词：RBF neural networks Adaptive tracking control H-infinity control Tracking error performance 

分 类 号：TP273[自动化与计算机技术—检测技术与自动化装置] O231[自动化与计算机技术—控制科学与工程]