Direct adaptive neural control for stabilization of nonlinear time-delay systems  

作　　者：WANG Min ZHANG SiYing CHEN Bing LUO Fei 

机构地区：[1]College of Automation, South China University of Technology, Guangzhou 510641, China [2]Institute of Complexity Science, Qingdao University, "'ingdao 266071, China

出　　处：《Science China(Information Sciences)》2010年第4期800-812,共13页中国科学（信息科学）（英文版）

基　　金：supported in part by the National Natural Science Foundation of China (Grant Nos. 90816028,60934001); Foundation of South China University of Technology (Grant Nos. 2009ZM0177, 2zdE5090770); the National Basic Research Program of China (Grant No. 2007CB311005)

摘　　要：This paper is concerned with the problem of adaptive neural control for uncertain nonlinear strictfeedback time-delay systems with unknown virtual control coefficients. Radial basis function （RBF） neural networks are employed to directly approximate unknown virtual control signals, and then the adaptive neural control law is constructed by Lyapunov-Krasovskii functionals and backstepping. In order to avoid encountering a large number of adaptive parameters when using RBF neural networks as function approximators, an unknown constant, instead of unknown neural weights themselves, is employed as the estimated parameter. This technique makes only one adaptive parameter tuned online, thus significantly alleviating the burdensome computation. Meanwhile, some continuous functions are introduced to overcome the design difficulty originating from the use of one adaptive parameter. The proposed adaptive control guarantees the boundedness of all the signals in the closed-loop system. Simulation studies are presented to illustrate the effectiveness of the scheme.This paper is concerned with the problem of adaptive neural control for uncertain nonlinear strictfeedback time-delay systems with unknown virtual control coefficients. Radial basis function （RBF） neural networks are employed to directly approximate unknown virtual control signals, and then the adaptive neural control law is constructed by Lyapunov-Krasovskii functionals and backstepping. In order to avoid encountering a large number of adaptive parameters when using RBF neural networks as function approximators, an unknown constant, instead of unknown neural weights themselves, is employed as the estimated parameter. This technique makes only one adaptive parameter tuned online, thus significantly alleviating the burdensome computation. Meanwhile, some continuous functions are introduced to overcome the design difficulty originating from the use of one adaptive parameter. The proposed adaptive control guarantees the boundedness of all the signals in the closed-loop system. Simulation studies are presented to illustrate the effectiveness of the scheme.

关 键 词：nonlinear time-delay systems neural control adaptive control BACKSTEPPING 

分 类 号：TP13[自动化与计算机技术—控制理论与控制工程] TP273.2[自动化与计算机技术—控制科学与工程]