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

被引：0
|
作者：
LIU YanJun [1 ]
LIU Lei [1 ]
TONG ShaoCheng [1 ]
机构：
[1] College of Science,Liaoning University of Technology
来源：
ScienceChina(InformationSciences) | 2014年 / 57卷 / 03期
基金：
中国国家自然科学基金;
关键词：
adaptive control; RBF neural network; non-symmetric dead-zone; backstepping design; uncertain nonlinear systems;
D O I：
暂无
中图分类号：
O231 [控制论（控制论的数学理论）];
学科分类号：
070105 ; 0711 ; 071101 ; 0811 ; 081101 ;
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 efectiveness of the proposed approach.
引用
收藏
页码：276 / 287
页数：12
相关论文
共 50 条
  • [41] Adaptive control of a class of discrete-time MIMO nonlinear systems with uncertain couplings
    Yang, Chenguang
    Li, Yanan
    Ge, Shuzhi Sam
    Lee, Tong Heng
    INTERNATIONAL JOURNAL OF CONTROL, 2010, 83 (10) : 2120 - 2133
  • [42] Adaptive Dynamic Surface Control for Finite-time Tracking of Uncertain Nonlinear Systems with Dead-zone Inputs and Actuator Faults
    Xue, Guangming
    Lin, Funing
    Li, Shenggang
    Liu, Heng
    INTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS, 2021, 19 (08) : 2797 - 2811
  • [43] Adaptive fuzzy-neural tracking control for uncertain nonlinear discrete-time systems in the NARMAX form
    Guo-Xing Wen
    Yan-Jun Liu
    Nonlinear Dynamics, 2011, 66 : 745 - 753
  • [44] Adaptive fuzzy-neural tracking control for uncertain nonlinear discrete-time systems in the NARMAX form
    Wen, Guo-Xing
    Liu, Yan-Jun
    NONLINEAR DYNAMICS, 2011, 66 (04) : 745 - 753
  • [45] Hybrid Neural Network Control for Uncertain Nonlinear Discrete-Time Systems with Bounded Disturbance
    Kumar, Rahul
    Singh, Uday Pratap
    Bali, Arun
    Raj, Kuldip
    WIRELESS PERSONAL COMMUNICATIONS, 2022, 126 (04) : 3475 - 3494
  • [46] Adaptive NN Controller Design for a Class of Nonlinear MIMO Discrete-Time Systems
    Liu, Yan-Jun
    Tang, Li
    Tong, Shaocheng
    Chen, C. L. Philip
    IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2015, 26 (05) : 1007 - 1018
  • [47] Adaptive tracking of a class of uncertain nonlinear systems subject to unknown dead-zone input nonlinearities: The symmetric and the non-symmetric cases
    Ibiir, Salim
    Xie, Wen Fang
    Su, Chun-Yi
    2006 AMERICAN CONTROL CONFERENCE, VOLS 1-12, 2006, 1-12 : 3391 - 3396
  • [48] Adaptive fuzzy output feedback control of uncertain nonlinear systems with nonsymmetric dead-zone input
    Zhou, Ning
    Liu, Yan-Jun
    Tong, Shao-Cheng
    NONLINEAR DYNAMICS, 2011, 63 (04) : 771 - 778
  • [49] Radial Basis Function Neural Network-based Control for Uncertain Nonlinear Systems with Unknown Dead-Zone Input
    Shahriari-kahkeshi, Maryam
    2017 5TH INTERNATIONAL CONFERENCE ON CONTROL, INSTRUMENTATION, AND AUTOMATION (ICCIA), 2017, : 6 - 11
  • [50] Adaptive fuzzy output feedback control of uncertain nonlinear systems with nonsymmetric dead-zone input
    Ning Zhou
    Yan-Jun Liu
    Shao-Cheng Tong
    Nonlinear Dynamics, 2011, 63 : 771 - 778
12345