Distributed optimization of multi-agent systems with delayed sampled-data

被引：14
|
作者：
Yan, Junxiu [1 ,2 ]
Yu, Hui [1 ,2 ]
Xia, Xiaohua [3 ]
机构：
[1] China Three Gorges Univ, Coll Sci, Yichang 443002, Peoples R China
[2] China Three Gorges Univ, Three Gorges Math Res Ctr, Yichang 443002, Peoples R China
[3] Univ Pretoria, Dept Elect Elect & Comp Engn, Ctr New Energy Syst, ZA-0002 Pretoria, South Africa
来源：
NEUROCOMPUTING | 2018年 / 296卷
关键词：
Distributed optimization; Consensus; Sampled-data; Multi-agent system; FINITE-TIME CONSENSUS; CONVEX-OPTIMIZATION; CONSTRAINED OPTIMIZATION; NETWORKS; AGENTS; COORDINATION; ALGORITHMS; LEADER;
D O I：
10.1016/j.neucom.2018.03.036
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In this paper, we study the distributed optimization problem of multi-agent systems with delayed sampled-data, where the interconnected topology is directed, weighted-balanced and strongly connected, and also local cost functions are strongly convex with globally Lipschitz gradients. Based on synchronous and asynchronous sampled-data, we construct two respective algorithms. Our main results, sufficient conditions for the convergence to an optimal solution, are obtained under assumption that all design parameters are chosen properly. We also present one example to validate our theoretical results. (C) 2018 Elsevier B.V. All rights reserved.
引用
收藏
页码：100 / 108
页数：9
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