Adaptive neural network tracking design for a class of uncertain nonlinear discrete-time systems with dead-zone  

作　　者：LIU YanJun LIU Lei TONG ShaoCheng 

机构地区：[1]College of Science,Liaoning University of Technology

出　　处：《Science China(Information Sciences)》2014年第3期271-282,共12页中国科学（信息科学）（英文版）

基　　金：supported in part by National Natural Science Foundation of China(Grant Nos.61074014,61104017);Program for Liaoning Innovative Research Team in University(Grant No.LT2012013);Program for Liaoning Excellent Talents in University(Grant No.LJQ2011064)

摘　　要：In this paper, the stability and control issues of a class of uncertain nonlinear discrete-time systems in the strict feedback form are investigated. The dead-zone input in the systems, whose property is non-symmetric and discretized, is investigated. The unknown functions in the systems are approximated by using the radial basis function neural networks （RBFNNs）. Backstepping design procedure is employed in the controller and the adaptation laws design. Lyapunov analysis method is utilized to prove the stability of the closed-loop system. A simulation example is given to illustrate the effectiveness of the proposed approach.In this paper, the stability and control issues of a class of uncertain nonlinear discrete-time systems in the strict feedback form are investigated. The dead-zone input in the systems, whose property is non-symmetric and discretized, is investigated. The unknown functions in the systems are approximated by using the radial basis function neural networks （RBFNNs）. Backstepping design procedure is employed in the controller and the adaptation laws design. Lyapunov analysis method is utilized to prove the stability of the closed-loop system. A simulation example is given to illustrate the effectiveness of the proposed approach.

关 键 词：adaptive control RBF neural network non-symmetric dead-zone backstepping design uncertainnonlinear systems 

分 类 号：O231[理学—运筹学与控制论]