Memoryless disturbance-observer-based adaptive tracking of uncertain pure-feedback nonlinear time-delay systems with unmatched disturbances

被引:5
|
作者
Kim, Hyoung Oh [1 ]
Yoo, Sung Jin [1 ]
机构
[1] Chung Ang Univ, Sch Elect & Elect Engn, 84 Heukseok Ro, Seoul 156756, South Korea
基金
新加坡国家研究基金会;
关键词
Nonlinear disturbance observer (NDO); Adaptive tracking; Unknown time-varying delays; Memoryless; Unmatched external disturbances; DYNAMIC SURFACE CONTROL; DEAD-ZONE NONLINEARITY; NEURAL-CONTROL; MISMATCHED DISTURBANCES; FUZZY-APPROXIMATION; TRAJECTORY TRACKING; MOTION CONTROLLER; ROBUST CONTROLLER; INPUT DELAY; STABILIZATION;
D O I
10.1016/j.isatra.2017.07.017
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
This paper presents a delay-independent nonlinear disturbance observer (NDO) design methodology for adaptive tracking of uncertain pure-feedback nonlinear systems in the presence of unknown time delays and unmatched external disturbances. Compared with all existing NDO-based control results for uncertain lower-triangular nonlinear systems where unknown time delays have been not considered, the main contribution of this paper is to develop a delay-independent design strategy to construct an NDO-based adaptive tracking scheme in the presence of unknown time-delayed nonlinearities and non-affine nonlinearities unmatched in the control input. The proposed delay-independent scheme is constructed by employing the appropriate Lyapunov-Krasovskii functionals and the same function approximators for the NDO and the controller. It is shown that all the signals of the closed-loop system are semi-globally uniformly ultimately bounded and the tracking error converges to an adjustable neighborhood of the origin. (C) 2017 ISA. Published by Elsevier Ltd. All rights reserved.
引用
收藏
页码:419 / 431
页数:13
相关论文
共 50 条
  • [41] Fuzzy-approximation-based decentralized adaptive control for pure-feedback large-scale nonlinear systems with time-delay
    Wang, Huanqing
    Yang, Xuebo
    Yu, Zhandong
    Liu, Kefu
    Liu, Xiaoping
    NEURAL COMPUTING & APPLICATIONS, 2015, 26 (01): : 151 - 160
  • [42] Adaptive Neural Tracking Control of Pure-feedback Nonlinear Systems
    Zhang, Tianping
    Zhu, Baicheng
    Shi, Xiaocheng
    PROCEEDINGS OF THE 2012 24TH CHINESE CONTROL AND DECISION CONFERENCE (CCDC), 2012, : 2122 - 2127
  • [43] Fuzzy-approximation-based decentralized adaptive control for pure-feedback large-scale nonlinear systems with time-delay
    Huanqing Wang
    Xuebo Yang
    Zhandong Yu
    Kefu Liu
    Xiaoping Liu
    Neural Computing and Applications, 2015, 26 : 151 - 160
  • [44] Neural Network-Based Adaptive Tracking Control for a Class of Uncertain Stochastic Nonlinear Pure-Feedback Systems
    Wang Rui
    Yu Fu-sheng
    Wang Jia-yin
    2013 25TH CHINESE CONTROL AND DECISION CONFERENCE (CCDC), 2013, : 495 - 500
  • [45] Observer-based adaptive control for uncertain time-delay systems
    Hua, CC
    Li, FL
    Guan, XP
    INFORMATION SCIENCES, 2006, 176 (02) : 201 - 214
  • [46] ADAPTIVE-OBSERVER FOR SYSTEMS WITH PURE TIME-DELAY
    IWAI, Z
    KAWASAKI, Y
    ELECTRONICS LETTERS, 1979, 15 (11) : 329 - 331
  • [47] Neural-network-based adaptive tracking control for nonlinear pure-feedback systems subject to periodic disturbance
    Zuo, Renwei
    Lv, Maolong
    Li, Yinghui
    Nie, Hongyan
    INTERNATIONAL JOURNAL OF CONTROL, 2022, 95 (09) : 2554 - 2564
  • [48] Approximation-based direct adaptive tracking control for a class of uncertain pure-feedback stochastic nonlinear systems
    Wang, Huanqing
    Chen, Bing
    Lin, Chong
    2013 25TH CHINESE CONTROL AND DECISION CONFERENCE (CCDC), 2013, : 13 - 18
  • [49] Adaptive NN Control for Uncertain Pure-Feedback Nonlinear Systems With State Constraints Subject to Unknown Disturbances
    Tang, Zhong-Liang
    Ge, Shuzhi Sam
    Tee, Keng Peng
    2015 54TH IEEE CONFERENCE ON DECISION AND CONTROL (CDC), 2015, : 7572 - 7577
  • [50] Observer-based adaptive neural control for a class of nonlinear pure-feedback systems
    Wang, Honghong
    Chen, Bing
    Lin, Chong
    Sun, Yumei
    NEUROCOMPUTING, 2016, 171 : 1517 - 1523