Distributed adaptive leader-follower and leaderless consensus control of a class of strict-feedback nonlinear systems: a unified approach

被引:135
作者
Huang, Jiangshuai [1 ]
Wang, Wei [2 ,3 ]
Wen, Changyun [4 ]
Zhou, Jing [5 ]
Li, Guoqi [6 ]
机构
[1] Chongqing Univ, Sch Automat, Chongqing 400044, Peoples R China
[2] Beihang Univ, Sch Automat Sci & Elect Engn, Beijing 100191, Peoples R China
[3] Beihang Univ, Beijing Adv Innovat Ctr Big Data & Brain Comp, Beijing 100191, Peoples R China
[4] Nanyang Technol Univ, Sch Elect & Elect Engn, Singapore 639798, Singapore
[5] Univ Agder, Dept Engn Sci, N-4898 Grimstad, Norway
[6] Tsinghua Univ, Dept Precis Instrument, Beijing 100084, Peoples R China
基金
中国国家自然科学基金;
关键词
Adaptive control; Distributed Consensus Control; Leader-follower Consensus; Leaderless Consensus; COOPERATIVE TRACKING CONTROL; LINEAR MULTIAGENT SYSTEMS; EULER-LAGRANGE SYSTEMS; UNKNOWN DYNAMICS; SYNCHRONIZATION; SEEKING; DESIGN;
D O I
10.1016/j.automatica.2020.109021
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, distributed adaptive consensus for a class of strict-feedback nonlinear systems under directed topology condition is investigated. Both leader-follower and leaderless cases are considered in a unified framework. To design distributed controller for each subsystem, a local compensatory variable is generated based on the signals collected from its neighbors. Such a technique enables us to solve the leader-follower consensus and leaderless consensus problems in a unified framework. And it further allows us to treat the leaderless consensus as a special case of the leader-follower consensus. For leader-follower consensus, the assumption that the leader trajectory is linearly parameterized with some known functions as required in most recent relevant literatures is successfully relaxed. It is shown that global uniform boundedness of all closed-loop signals and asymptotically output consensus could be achieved for both cases. Simulation results are provided to verify the effectiveness of our schemes. (C) 2020 Elsevier Ltd. All rights reserved.
引用
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页数:9
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