Prescribed performance distributed consensus control for nonlinear multi-agent systems with unknown dead-zone input

被引：37
|
作者：
Cui, Guozeng [1 ]
Xu, Shengyuan [1 ]
Ma, Qian [1 ]
Li, Yongmin [2 ]
Zhan, Zhengqiang [3 ]
机构：
[1] Nanjing Univ Sci & Technol, Sch Automat, Nanjing, Jiangsu, Peoples R China
[2] Huzhou Teachers Coll, Sch Sci, Huzhou, Zhejiang, Peoples R China
[3] Qufu Normal Univ, Sch Elect Engn & Automat, Rizhao, Peoples R China
来源：
INTERNATIONAL JOURNAL OF CONTROL | 2018年 / 91卷 / 05期
基金：
中国国家自然科学基金;
关键词：
Output consensus; nonlinear multi-agent systems; dead-zone; fuzzy logical systems; COOPERATIVE TRACKING CONTROL; OUTPUT-FEEDBACK CONTROL; ADAPTIVE NEURAL-CONTROL; DYNAMIC SURFACE CONTROL; TIME DELAYS; NETWORKS; SYNCHRONIZATION; UNCERTAINTIES; FORM;
D O I：
10.1080/00207179.2017.1305510
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
In this paper, the problem of prescribed performance distributed output consensus for higher-order non-affine nonlinear multi-agent systems with unknown dead-zone input is investigated. Fuzzy logical systems are utilised to identify the unknown nonlinearities. By introducing prescribed performance, the transient and steady performance of synchronisation errors are guaranteed. Based on Lyapunov stability theory and the dynamic surface control technique, a new distributed consensus algorithm for non-affine nonlinear multi-agent systems is proposed, which ensures cooperatively uniformly ultimately boundedness of all signals in the closed-loop systems and enables the output of each follower to synchronise with the leader within predefined bounded error. Finally, simulation examples are provided to demonstrate the effectiveness of the proposed control scheme.
引用
收藏
页码：1053 / 1065
页数：13
相关论文
共 50 条
  • [1] Adaptive Consensus Tracking Control for Nonlinear Multi-Agent Systems with Input Saturation and Dead-Zone
    Li, Shubo
    Pan, Yingnan
    Liang, Hongjing
    [J]. 2019 IEEE SYMPOSIUM SERIES ON COMPUTATIONAL INTELLIGENCE (IEEE SSCI 2019), 2019, : 3281 - 3286
  • [2] Distributed adaptive coordination control for uncertain nonlinear multi-agent systems with dead-zone input
    Wang, Fang
    Liu, Zhi
    Zhang, Yun
    Chen, Bing
    [J]. JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS, 2016, 353 (10): : 2270 - 2289
  • [3] Improved prescribed performance control for nonlinear systems with unknown control direction and input dead-zone
    Huang, Zongsheng
    Li, Tieshan
    Long, Yue
    Yang, Hanqing
    Chen, C. L. Philip
    Liang, Hongjing
    [J]. INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL, 2024, 34 (07) : 4489 - 4508
  • [4] Adaptive Neural Consensus Tracking Control of Uncertain Nonlinear Multi-agent Systems with Unknown Output Dead-zone
    Yang, Hainan
    Lian, Yingzhan
    Guo, Li
    Hong, Zhucai
    Xu, Zhenyi
    Cui, Xuming
    [J]. PROCEEDINGS OF THE 38TH CHINESE CONTROL CONFERENCE (CCC), 2019, : 5933 - 5938
  • [5] Consensus of multi-agent systems with dead-zone nonlinearity
    Haibo Jiang
    Liping Zhang
    Jianjiang Yu
    Caigen Zhou
    [J]. International Journal of Control, Automation and Systems, 2012, 10 : 824 - 829
  • [6] Consensus of Multi-agent Systems with Dead-zone Nonlinearity
    Jiang, Haibo
    Zhang, Liping
    Yu, Jianjiang
    Zhou, Caigen
    [J]. INTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS, 2012, 10 (04) : 824 - 829
  • [7] Finite-time consensus control for a class of multi-agent systems with dead-zone input
    Yao, Dajie
    Dou, Chunxia
    Zhao, Nan
    Zhang, Tingjun
    [J]. JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS, 2021, 358 (07): : 3512 - 3529
  • [8] Adaptive consensus for heterogeneous unknown nonlinear multi-agent systems with asymmetric input dead-zone: A finite-time approach
    Zamanian, Mehdi
    Abdollahi, Farzaneh
    Nikravesh, Seyyed Kamaleddin Yadavar
    [J]. TRANSACTIONS OF THE INSTITUTE OF MEASUREMENT AND CONTROL, 2022, 44 (01) : 105 - 120
  • [9] Fuzzy prescribed-time self-triggered consensus control for nonlinear multi-agent systems with dead-zone output
    Yan, Yancheng
    Li, Tieshan
    Wang, Jianhui
    Chen, C.L. Philip
    Liang, Hongjing
    [J]. Fuzzy Sets and Systems, 2024, 492
  • [10] Neuro-Based Consensus Seeking for Nonlinear Uncertainty Multi-Agent Systems Constrained by Dead-Zone Input
    Qin, Zhenhua
    Gai, Rongjun
    [J]. INTERNATIONAL JOURNAL ON SEMANTIC WEB AND INFORMATION SYSTEMS, 2023, 19 (01)
12345