A modified distributed optimization method for both continuous-time and discrete-time multi-agent systems

被引：13
|
作者：
Wang, Dong [1 ]
Wang, Wei [1 ]
Liu, Yurong [1 ,2 ,3 ]
Alsaadi, Fuad E. [3 ]
机构：
[1] Dalian Univ Technol, Sch Control Sci & Engn, Dalian 116024, Peoples R China
[2] Yangzhou Univ, Dept Math, Yangzhou 225002, Jiangsu, Peoples R China
[3] King Abdulaziz Univ, Fac Engn, CSN Res Grp, Jeddah 21589, Saudi Arabia
来源：
NEUROCOMPUTING | 2018年 / 275卷
基金：
中国国家自然科学基金;
关键词：
Multi-agent systems; Cost function; Convex optimization; Convergence rate; Lyapunov method; TRACKING CONTROL; CONSTRAINED OPTIMIZATION; SWITCHING TOPOLOGIES; CONTAINMENT CONTROL; CONSENSUS; LEADER; COMMUNICATION; ALGORITHM; NETWORKS;
D O I：
10.1016/j.neucom.2017.09.016
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
This paper discusses the distributed optimization problem for the continuous-time and discrete-time multi-agent systems. For such a problem, each agent possesses a local convex cost function only known by itself and all the agents converge to the optimizer of the sum of the local cost function through estimating the optimal states of the local cost function and exchanging states information between agents. Sufficient conditions for convergence to the optimizer of the continuous-time and discrete-time algorithms are provided by making use of the Lyapunov method. We also obtain the least convergence rate for the modified algorithm. Moreover, numerical simulations are supplied to testify the results we present. (C) 2017 Elsevier B.V. All rights reserved.
引用
收藏
页码：725 / 732
页数：8
相关论文
共 50 条
  • [21] A projection-based continuous-time algorithm for distributed optimization over multi-agent systems
    Xingnan Wen
    Sitian Qin
    Complex & Intelligent Systems, 2022, 8 : 719 - 729
  • [22] Distributed optimisation design with triggers for disturbed continuous-time multi-agent systems
    Deng, Zhenhua
    Wang, Xinghu
    Hong, Yiguang
    IET CONTROL THEORY AND APPLICATIONS, 2017, 11 (02): : 282 - 290
  • [23] Distributed Optimal Consensus Control Algorithm for Continuous-Time Multi-Agent Systems
    Wang, Qishao
    Duan, Zhisheng
    Wang, Jingyao
    IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS, 2020, 67 (01) : 102 - 106
  • [24] Decomposition-Based Distributed Control for Continuous-Time Multi-Agent Systems
    Ghadami, Rasoul
    Shafai, Bahram
    IEEE TRANSACTIONS ON AUTOMATIC CONTROL, 2013, 58 (01) : 258 - 264
  • [25] Distributed inverse optimal control for discrete-time nonlinear multi-agent systems
    Belfo, Joao P.
    Pedro Aguiar, A.
    Lemos, Joao M.
    2021 AMERICAN CONTROL CONFERENCE (ACC), 2021, : 2661 - 2666
  • [26] Consensus with Distributed LQR Control Algorithm for Discrete-time Multi-agent Systems
    Li, Xiaoqian
    Wang, Wei
    2017 29TH CHINESE CONTROL AND DECISION CONFERENCE (CCDC), 2017, : 6307 - 6312
  • [27] Distributed Inverse Optimal Control for Discrete-Time Nonlinear Multi-Agent Systems
    Belfo, Joao P.
    Aguiar, A. Pedro
    Lemos, Joao M.
    IEEE CONTROL SYSTEMS LETTERS, 2021, 5 (06): : 2096 - 2101
  • [28] On formability of linear continuous-time multi-agent systems
    Cuiqin Ma
    Jifeng Zhang
    Journal of Systems Science and Complexity, 2012, 25 : 13 - 29
  • [29] ON FORMABILITY OF LINEAR CONTINUOUS-TIME MULTI-AGENT SYSTEMS
    Cuiqin MA
    Jifeng ZHANG
    JournalofSystemsScience&Complexity, 2012, 25 (01) : 13 - 29
  • [30] A continuous-time neurodynamic approach and its discretization for distributed convex optimization over multi-agent systems
    Wen, Xingnan
    Luan, Linhua
    Qin, Sitian
    NEURAL NETWORKS, 2021, 143 : 52 - 65
12345