Decentralized learning control for large-scale systems with gain-adaptation mechanisms

被引：3
|
作者：
Jiang, Hao [1 ]
He, Xun [1 ]
Song, Qijiang [1 ]
Shen, Dong [1 ]
机构：
[1] Renmin Univ China, Sch Math, Beijing 100872, Peoples R China
来源：
INFORMATION SCIENCES | 2023年 / 623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：
Large-scale systems; State-coupling matrix; Decentralized learning control; Gain adaptation; NONLINEAR-SYSTEMS; TRACKING; SCHEME;
D O I：
10.1016/j.ins.2022.12.043
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 study investigated the gain-adaptation mechanism of decentralized learning control for large-scale interconnected systems subject to measurement noise. The control objective is to minimize asymptotically averaged tracking errors in the iteration domain. The state-coupling matrix concept is employed to model the interactions among subsystems. Decentralized learning control schemes are proposed with three gain sequences: a prede-fined decreasing gain sequence, global performance-adaptive gain sequence, and decen-tralized adaptive gain sequence. The input sequences generated by the proposed schemes are shown to be convergent in the mean-square sense. Illustrative simulations are performed to verify the theoretical results.(c) 2022 Elsevier Inc. All rights reserved.
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页码：539 / 558
页数：20
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