An Adaptive Multi-Agent System With Duplex Control Laws for Distributed Resource Allocation

被引:27
作者
Guo, Zhenyuan [1 ,2 ]
Lian, Mengke [1 ,2 ]
Wen, Shiping [3 ]
Huang, Tingwen [4 ]
机构
[1] Hunan Univ, Sch Math, Changsha 410082, Peoples R China
[2] Hunan Univ, Hunan Prov Key Lab Intelligent Informat Proc & Ap, Changsha 410082, Peoples R China
[3] Univ Technol Sydney, Fac Engn Informat Technol, Ctr Artificial Intelligence, Ultimo, NSW 2007, Australia
[4] Texas A&M Univ Qatar, Sci Program, Doha 23874, Qatar
来源
IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING | 2022年 / 9卷 / 02期
基金
中国国家自然科学基金;
关键词
Resource management; Optimization; Multi-agent systems; Multiplexing; Convex functions; Topology; Eigenvalues and eigenfunctions; Resource allocation; distributed optimization; exact penalty method; duplex control laws; ECONOMIC-DISPATCH; CONVEX-OPTIMIZATION; ALGORITHMS; INITIALIZATION; COORDINATION; CONSENSUS; CONSTRAINTS; NETWORK; DESIGN;
D O I
10.1109/TNSE.2021.3117881
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
In this paper, we present an adaptive multi-agent system with duplex control laws for non-smooth resource allocation problem, where the decisions are subjected to local constraints and network resource constraints. The multi-agent system based on the distance penalty function method is developed in three sets of coupled differential inclusions or equations, where the last set of differential equations are designated to learn an adaptive penalty vector. In the multi-agent system, proportional and integral controls can be performed from two different layers of the multiplex control network with an independent communication topology at each layer. The existence of equilibrium points and convergence of the multi-agent system are proven for achieving optimal resource allocation starting from any initial resource allocation. Finally, the simulation results of two illustrative examples are discussed to substantiate the theoretical results.
引用
收藏
页码:389 / 400
页数:12
相关论文
共 51 条
[1]   Distributed Holistic Framework for Smart City Infrastructures: Tale of Interdependent Electrified Transportation Network and Power Grid [J].
Amini, M. Hadi ;
Mohammadi, Javad ;
Kar, Soummya .
IEEE ACCESS, 2019, 7 :157535-157554
[2]   Distributed Control of Networked Dynamical Systems: Static Feedback, Integral Action and Consensus [J].
Andreasson, Martin ;
Dimarogonas, Dimos V. ;
Sandberg, Henrik ;
Johansson, Karl Henrik .
IEEE TRANSACTIONS ON AUTOMATIC CONTROL, 2014, 59 (07) :1750-1764
[3]  
[Anonymous], 2018, Springer Optim. Appl.
[4]  
Bauschke HH, 2011, CMS BOOKS MATH, P1, DOI 10.1007/978-1-4419-9467-7
[5]   Distributed Consensus-Based Economic Dispatch With Transmission Losses [J].
Binetti, Giulio ;
Davoudi, Ali ;
Lewis, Frank L. ;
Naso, David ;
Turchiano, Biagio .
IEEE TRANSACTIONS ON POWER SYSTEMS, 2014, 29 (04) :1711-1720
[6]  
Burbano D, 2015, IEEE DECIS CONTR P, P4854, DOI 10.1109/CDC.2015.7402977
[7]   Distributed constrained optimization for multi-agent networks with nonsmooth objective functions [J].
Chen, Gang ;
Yang, Qing .
SYSTEMS & CONTROL LETTERS, 2019, 124 :60-67
[8]   Initialization-free distributed coordination for economic dispatch under varying loads and generator commitment [J].
Cherukuri, Ashish ;
Cortes, Jorge .
AUTOMATICA, 2016, 74 :183-193
[9]   Distributed Generator Coordination for Initialization and Anytime Optimization in Economic Dispatch [J].
Cherukuri, Ashish ;
Cortes, Jorge .
IEEE TRANSACTIONS ON CONTROL OF NETWORK SYSTEMS, 2015, 2 (03) :226-237
[10]   Distributed Continuous-Time Algorithms for Resource Allocation Problems Over Weight-Balanced Digraphs [J].
Deng, Zhenhua ;
Liang, Shu ;
Hong, Yiguang .
IEEE TRANSACTIONS ON CYBERNETICS, 2018, 48 (11) :3116-3125