Dynamic surface error constrained adaptive fuzzy output feedback control for switched nonlinear systems with unknown dead zone

被引:16
|
作者
Zhang, Lili [1 ,2 ]
Yang, Guang-Hong [1 ,2 ]
机构
[1] Northeastern Univ, Coll Informat Sci & Engn, Shenyang 110819, Peoples R China
[2] Northeastern Univ, State Key Lab Synthet Automat Proc Ind, Shenyang 110819, Peoples R China
关键词
Switched nonlinear systems; Prescribed performance; Average dwell time; Unknown dead zone; DSC; NEURAL-CONTROL; TRACKING CONTROL; DESIGN; STABILIZATION; NETWORKS; FORM;
D O I
10.1016/j.neucom.2016.03.028
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
This paper investigates the adaptive fuzzy output-feedback dynamic surface control (DSC) approach with prescribed performance for a class of uncertain switched nonlinear systems with unknown dead-zone. In this research, fuzzy logic systems are used to identify the unknown nonlinear functions, a fuzzy switched state observer is established to observe the unmeasured states. Based on DSC backstepping control design technique and incorporated by the predefined performance theory and the average dwell time method, a new adaptive fuzzy output-feedback control method is developed. It is proved that the proposed control approach can ensure that all the signals of the resulting closed-loop system are bounded, and the tracking errors are within the prescribed performance bounds for all times. Simulation studies illustrate the effectiveness of the proposed approach. (C) 2016 Elsevier B.V. All rights reserved.
引用
收藏
页码:128 / 136
页数:9
相关论文
共 50 条
  • [31] An adaptive dynamic surface control of output constrained stochastic nonlinear systems with unknown control directions
    Shen, Fei
    Wang, Xinjun
    Yin, Xinghui
    Jin, Lingling
    INTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING, 2020, 34 (08) : 1013 - 1034
  • [32] Adaptive fuzzy dynamic surface control for a class of nonlinear systems with fuzzy dead zone and dynamic uncertainties
    Fang Wang
    Zhi Liu
    Yun Zhang
    Xin Chen
    C. L. Philip Chen
    Nonlinear Dynamics, 2015, 79 : 1693 - 1709
  • [33] Decentralized Prescribed Performance Adaptive Output Feedback Control for Nonlinear Systems with Unknown Dead-Zone
    Li, Shi
    Zhang, Tianping
    Ge, Jiwei
    Wang, Min
    Xue, Bei
    PROCEEDINGS OF THE 28TH CHINESE CONTROL AND DECISION CONFERENCE (2016 CCDC), 2016, : 6468 - 6473
  • [34] Adaptive fuzzy dynamic surface control for a class of nonlinear systems with fuzzy dead zone and dynamic uncertainties
    Wang, Fang
    Liu, Zhi
    Zhang, Yun
    Chen, Xin
    Chen, C. L. Philip
    NONLINEAR DYNAMICS, 2015, 79 (03) : 1693 - 1709
  • [35] Fuzzy Adaptive Output Feedback Control for MIMO Switched Nontriangular Structure Nonlinear Systems With Unknown Control Directions
    Huang, Leitao
    Li, Yongming
    Tong, Shaocheng
    IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS, 2020, 50 (02): : 550 - 564
  • [36] Adaptive Fuzzy Dynamic Surface Control of Nonlinear Constrained Systems With Unknown Virtual Control Coefficients
    Wang, Lijie
    Chen, C. L. Philip
    IEEE TRANSACTIONS ON FUZZY SYSTEMS, 2020, 28 (08) : 1737 - 1747
  • [37] Error-driven nonlinear feedback-based fuzzy adaptive output dynamic surface control for nonlinear systems with partially constrained tracking errors
    Gao, Shigen
    Dong, Hairong
    Ning, Bin
    Wang, Hongwei
    JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS, 2018, 355 (13): : 5452 - 5474
  • [38] Adaptive fuzzy control for pure-feedback stochastic nonlinear systems with unknown dead zone outputs
    Su, Hang
    Zhang, Weihai
    INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE, 2018, 49 (14) : 2981 - 2995
  • [39] Nonlinear Filters-Based Adaptive Fuzzy Control of Strict-Feedback Nonlinear Systems With Unknown Asymmetric Dead-Zone Output
    Ma, Zhiyao
    Tong, Shaocheng
    IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING, 2023, 21 (04) : 1 - 11
  • [40] Adaptive Neural Dynamic Surface Control for Nonstrict-Feedback Systems With Output Dead Zone
    Shi, Xiaocheng
    Lim, Cheng-Chew
    Shi, Peng
    Xu, Shengyuan
    IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2018, 29 (11) : 5200 - 5213