Consensus-based iterative learning of heterogeneous agents with application to distributed optimization

被引:20
|
作者
Song, Qiang [1 ,2 ]
Meng, Deyuan [3 ,4 ]
Liu, Fang [1 ,2 ]
机构
[1] Huanghuai Univ, Henan Int Joint Lab Behav Optimizat Control Smart, Zhumadian 463000, Henan, Peoples R China
[2] Huanghuai Univ, Sch Informat Engn, Zhumadian 463000, Henan, Peoples R China
[3] Beihang Univ BUAA, Res Div 7, Beijing 100191, Peoples R China
[4] Beihang Univ BUAA, Sch Automat Sci & Elect Engn, Beijing 100191, Peoples R China
基金
中国国家自然科学基金;
关键词
Iterative learning control; Multi-agent system; Consensus; Distributed convex optimization; Switching topology; CONVEX-OPTIMIZATION; MULTIAGENT SYSTEMS; CONVERGENCE; NETWORKS;
D O I
10.1016/j.automatica.2021.110096
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 deals with the distributed iterative learning control (ILC) problem of leaderless consensus in a network of heterogeneous nonlinear agents. For a general case that the topology graph is dynamically changing with respect to both iteration and time axes, an ILC-based consensus protocol is designed for each agent by utilizing its control input and neighboring information from the last iteration. It is shown that under a basic joint spanning tree condition, the states of all agents can exponentially agree on a common trajectory along the iteration axis. Interestingly, by the appropriate design of the agents' dynamics, it is found that the consensus trajectory and the network state can approach the unique optimal point of a convex optimization problem as the time evolves. Simulations demonstrate the validity of the proposed algorithms and theoretical analysis. (C) 2021 Elsevier Ltd. All rights reserved.
引用
收藏
页数:8
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