Robust cluster consensus of general fractional-order nonlinear multi agent systems via adaptive sliding mode controller

被引:34
|
作者
Yaghoubi, Zahra [1 ]
机构
[1] Amirkabir Univ Technol, Dept Elect Engn, Tehran, Iran
关键词
Robust cluster consensus; Fractional-order multi agent systems; Adaptive sliding mode controller; External disturbances; Dynamic uncertainty; TRACKING; STABILITY; NETWORKS;
D O I
10.1016/j.matcom.2020.01.002
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
In this paper robust cluster consensus is investigated for general fractional-order multi agent systems with nonlinear dynamics with dynamic uncertainty and external disturbances via adaptive sliding mode controller. First, robust cluster consensus for general fractional-order nonlinear multi agent systems is investigated with dynamic uncertainty and external disturbances in which multi agent systems are weakly heterogeneous because they have identical nominal dynamics with different norm-bounded parameter uncertainties. Then, robust cluster consensus for the fractional-order nonlinear multi agent systems with general form dynamics is investigated by using adaptive sliding mode controller. Robust cluster consensus for general fractional-order nonlinear multi agent systems is achieved asymptotically without disturbance. It is shown that the errors between agents can converge to a small region in the presence of disturbances based on the linear matrix inequality (LMI) and Mittag-Leffler stability theory. Finally, simulation examples are presented for general form multi agent systems, i.e. a single-link flexible joint manipulator which demonstrates the efficiency of the proposed adaptive controller. (C) 2020 International Association for Mathematics and Computers in Simulation (IMACS). Published by Elsevier B.V. All rights reserved.
引用
收藏
页码:15 / 32
页数:18
相关论文
共 50 条
  • [1] Cluster consensus of general fractional-order nonlinear multi agent systems via adaptive sliding mode controller
    Yaghoubi, Zahra
    Talebi, Heidar Ali
    ARCHIVES OF CONTROL SCIENCES, 2019, 29 (04): : 643 - 665
  • [2] ROBUST CLUSTER CONSENSUS OF GENERAL FRACTIONAL-ORDER NONLINEAR MULTI-AGENT SYSTEMS WITH DYNAMIC UNCERTAINTY
    Yaghoubi, Zahra
    Talebi, Heidar A.
    MECHATRONIC SYSTEMS AND CONTROL, 2020, 48 (03): : 165 - 170
  • [3] ROBUST CLUSTER CONSENSUS OF HIGH FRACTIONAL-ORDER NONLINEAR MULTI-AGENT SYSTEMS WITH EXTERNAL DISTURBANCES
    Yaghoubi, Zahra
    MECHATRONIC SYSTEMS AND CONTROL, 2020, 48 (04): : 249 - 255
  • [4] Distributed robust adaptive consensus control for uncertain nonlinear fractional-order multi-agent systems
    Gong, Ping
    PROCEEDINGS OF THE 2019 31ST CHINESE CONTROL AND DECISION CONFERENCE (CCDC 2019), 2019, : 1380 - 1385
  • [5] Adaptive consensus control of nonlinear fractional-order multi-agent systems with a leader
    Yang, JiaJun
    Luo, Wei
    Yi, Hao
    Xu, Wenqiang
    2019 3RD INTERNATIONAL SYMPOSIUM ON AUTONOMOUS SYSTEMS (ISAS 2019), 2019, : 528 - 533
  • [6] Consensus of a class of nonlinear fractional-order multi-agent systems via dynamic output feedback controller
    Zavvari, Elyar
    Badri, Pouya
    Sojoodi, Mahdi
    TRANSACTIONS OF THE INSTITUTE OF MEASUREMENT AND CONTROL, 2022, 44 (06) : 1228 - 1246
  • [7] Consensus of second-order nonlinear multi-agent systems via sliding mode observer and controller
    Li, Xiaolei
    Luo, Xiaoyuan
    Li, Shaobao
    Li, Jianjin
    Guan, Xinping
    JOURNAL OF SYSTEMS ENGINEERING AND ELECTRONICS, 2017, 28 (04) : 756 - 765
  • [8] Consensus Tracking of Fractional-Order Multi-Agent Systems Based on Sliding Mode Estimator
    Bai, Jing
    Wen, Guoguang
    Rahmani, Ahmed
    Yu, Yongguang
    PROCEEDINGS OF THE 35TH CHINESE CONTROL CONFERENCE 2016, 2016, : 7944 - 7949
  • [9] Adaptive bipartite output consensus of nonlinear fractional-order multi-agent systems
    Mahmoodi, Hadi
    Shojaei, Khoshnam
    INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE, 2022, 53 (08) : 1615 - 1638
  • [10] Robust consensus of fractional-order multi-agent systems with input saturation and external disturbances
    Chen, Lin
    Wang, Yan-Wu
    Yang, Wu
    Xiao, Jiang-Wen
    NEUROCOMPUTING, 2018, 303 : 11 - 19