Distributed optimization for uncertain nonlinear interconnected multi-agent systems

被引：2
|
作者：
An, Baizheng [1 ]
Huang, Bomin [1 ]
Zou, Yao [2 ,3 ]
Chen, Fei [1 ,4 ]
Meng, Ziyang [5 ]
机构：
[1] Northeastern Univ Qinhuangdao, Sch Control Engn, Qinhuangdao 066004, Peoples R China
[2] Univ Sci & Technol Beijing, Sch Automat & Elect Engn, Beijing 100083, Peoples R China
[3] Univ Sci & Technol Beijing, Inst Artificial Intelligence, Beijing 100083, Peoples R China
[4] Northeastern Univ, State Key Lab Synthet Automation Proc Ind, Shenyang 110004, Peoples R China
[5] Tsinghua Univ, Dept Precis Instrument, Beijing 100084, Peoples R China
来源：
SYSTEMS & CONTROL LETTERS | 2022年 / 168卷
基金：
中国国家自然科学基金; 北京市自然科学基金;
关键词：
Convex optimization; Interconnected system; Distributed control; CONSENSUS; TRACKING;
D O I：
10.1016/j.sysconle.2022.105364
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
Distributed optimization for uncertain nonlinear interconnected multi-agent systems is considered in this paper. The objective is to design distributed algorithms by using local information such that all the agents' outputs converge to the global optimal output. The problem is challenging due to unknown modeling uncertainties and nonlinear interconnections. First, a distributed algorithm composed of a distributed state observer and a distributed optimal output observer is presented for heterogeneous outputs. Next, it is shown that the objective can be achieved by employing solely a distributed optimal output observer if the agents' outputs are homogeneous. Finally, the algorithms' effectiveness is validated by an electrical power system composed of four-machine subsystems. (C) 2022 Elsevier B.V. All rights reserved.
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页数：6
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