Distributed Optimization for Multi-Agent Systems With Time Delay

被引：11
作者：
Yang, Zhengquan [1 ]
Pan, Xiaofang [1 ]
Zhang, Qing [1 ]
Chen, Zengqiang [2 ]
机构：
[1] Civil Aviat Univ China, Coll Sci, Tianjin 300300, Peoples R China
[2] Nankai Univ China, Coll Artificial Intelligence, Tianjin 300350, Peoples R China
来源：
IEEE ACCESS | 2020年 / 8卷
基金：
中国国家自然科学基金;
关键词：
Distributed optimization; multi-agent systems; time delay; Lyapunov-Krasovskii function; zero-gradient-sum algorithm; CONVEX-OPTIMIZATION; SUBGRADIENT METHODS; CONSENSUS; GRADIENT; ALGORITHMS; NETWORKS;
D O I：
10.1109/ACCESS.2020.3007731
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
The distributed optimization for multi-agent systems with time delay and first-order is investigated in this paper. The objective of the distributed optimization is to optimize the objective function composed of the sum of local objective functions, which can only be known by its corresponding agents. Firstly, a distributed algorithm for time-delay systems is proposed to solve the optimization problem that each agent depends on its own state and the state between itself and its neighbors. Secondly, Lyapunov-Krasovskii function is used to prove that the states of each agent can be asymptotically the same, and the states are optimal. Finally, an example is given for illustrating the analytical results and a comparison is also gave to illustrate the differences between the algorithm of this paper and other results.
引用
收藏
页码：123019 / 123025
页数：7
相关论文
共 50 条
  • [21] Differentially private consensus and distributed optimization in multi-agent systems: A review
    Wang, Yamin
    Lin, Hong
    Lam, James
    Kwok, Ka-Wai
    NEUROCOMPUTING, 2024, 597
  • [22] Containment control of multi-agent systems with time delay
    Yan, Fang
    Xie, Dongmei
    TRANSACTIONS OF THE INSTITUTE OF MEASUREMENT AND CONTROL, 2014, 36 (02) : 196 - 205
  • [23] A modified distributed optimization method for both continuous-time and discrete-time multi-agent systems
    Wang, Dong
    Wang, Wei
    Liu, Yurong
    Alsaadi, Fuad E.
    NEUROCOMPUTING, 2018, 275 : 725 - 732
  • [24] A Multi-Agent System With a Proportional-Integral Protocol for Distributed Constrained Optimization
    Yang, Shaofu
    Liu, Qingshan
    Wang, Jun
    IEEE TRANSACTIONS ON AUTOMATIC CONTROL, 2017, 62 (07) : 3461 - 3467
  • [25] Distributed Subgradient Methods for Multi-Agent Optimization
    Nedic, Angelia
    Ozdaglar, Asurrian
    IEEE TRANSACTIONS ON AUTOMATIC CONTROL, 2009, 54 (01) : 48 - 61
  • [26] Inexact dual averaging method for distributed multi-agent optimization
    Yuan, Deming
    Ho, Daniel W. C.
    Xu, Shengyuan
    SYSTEMS & CONTROL LETTERS, 2014, 71 : 23 - 30
  • [27] Dual decomposition for multi-agent distributed optimization with coupling constraints*
    Falsone, Alessandro
    Margellos, Kostas
    Garatti, Simone
    Prandini, Maria
    AUTOMATICA, 2017, 84 : 149 - 158
  • [28] Stochastic mirror descent method for distributed multi-agent optimization
    Li, Jueyou
    Li, Guoquan
    Wu, Zhiyou
    Wu, Changzhi
    OPTIMIZATION LETTERS, 2018, 12 (06) : 1179 - 1197
  • [29] Distributed hybrid optimization for multi-agent systems
    XueGang Tan
    Yang Yuan
    WangLi He
    JinDe Cao
    TingWen Huang
    Science China Technological Sciences, 2022, 65 : 1651 - 1660
  • [30] Distributed optimization via multi-agent systems
    Wang L.
    Lu K.-H.
    Guan Y.-Q.
    Kongzhi Lilun Yu Yingyong/Control Theory and Applications, 2019, 36 (11): : 1820 - 1833
12345