ENGINEERING Information Technology & Electronic Engineering  2026 Vol.27 No.6 P.1-14

http://doi.org/10.1631/ENG.ITEE.2026.0132


Distributed control and decision-making algorithms for open multi-agent systems: a brief overview


Author(s):  Guanghui WEN, Meng LUAN, Xiao FANG, Xiaodong LI

Affiliation(s):  1. School of Automation, Southeast University, Nanjing 210096, China more

Corresponding email(s):   ghwen@seu.edu.cn

Key Words:  Open multi-agent system, Time-varying topology, Distributed control, Distributed optimization, Nash equilibrium seeking


Share this article to: More |Next Article >>>

Guanghui WEN, Meng LUAN, Xiao FANG, Xiaodong LI. Distributed control and decision-making algorithms for open multi-agent systems: a brief overview[J]. Journal of Zhejiang University Science C, 2026, 27(6): 1-14.

@article{title="Distributed control and decision-making algorithms for open multi-agent systems: a brief overview",
author="Guanghui WEN, Meng LUAN, Xiao FANG, Xiaodong LI",
journal="Journal of Zhejiang University Science C",
volume="27",
number="6",
pages="1-14",
year="2026",
publisher="Zhejiang University Press & Springer",
doi="10.1631/ENG.ITEE.2026.0132"
}

%0 Journal Article
%T Distributed control and decision-making algorithms for open multi-agent systems: a brief overview
%A Guanghui WEN
%A Meng LUAN
%A Xiao FANG
%A Xiaodong LI
%J Frontiers of Information Technology & Electronic Engineering
%V 27
%N 6
%P 1-14
%@ 1869-1951
%D 2026
%I Zhejiang University Press & Springer
%DOI 10.1631/ENG.ITEE.2026.0132

TY - JOUR
T1 - Distributed control and decision-making algorithms for open multi-agent systems: a brief overview
A1 - Guanghui WEN
A1 - Meng LUAN
A1 - Xiao FANG
A1 - Xiaodong LI
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 27
IS - 6
SP - 1
EP - 14
%@ 1869-1951
Y1 - 2026
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/ENG.ITEE.2026.0132


Abstract: 
open multi-agent systems (OMASs), characterized by the dynamic joining and leaving of agents, possess distinct attributes such as agent-level autonomy, time-varying network topologies, and environmental openness. These characteristics make them highly applicable to dynamic scenarios like robotic swarms, smart grids, and vehicular networks. However, such dynamism introduces core challenges in maintaining system stability, achieving efficient collaboration, and guaranteeing decision robustness. This paper presents a brief overview of recent advances in distributed control and decision-making algorithms for OMASs. First, the fundamental concepts and control strategies of OMASs are systematically reviewed. Second, distributed decision-making mechanisms encompassing distributed consensus optimization, separable resource allocation, and Nash equilibrium (NE) seeking in non-cooperative games are discussed, highlighting key technologies and typical methods. Finally, an outlook on future perspectives in the field is presented.

开放多智能体系统的分布式控制与决策算法:简要综述

温广辉1,栾萌2,房肖1,李晓东3
1东南大学自动化学院,中国南京市,210096
2岭南大学数据科学学院工业数据科学学部,中国香港特别行政区
3东南大学数学学院,中国南京市,211189
摘要:开放多智能体系统以智能体的动态加入与退出为主要特征,具备个体自主性、网络拓扑时变性以及环境开放性等显著属性。这些特性使其在机器人集群、智能电网和车联网等动态场景中具有极高应用价值。然而,这种高度动态性也为维持系统稳定性、实现高效协同和保证决策鲁棒性带来核心挑战。本文简要综述了面向开放多智能体系统的分布式控制与决策算法的最新研究进展。首先,系统梳理了开放多智能体系统的基本概念与控制策略。其次,探讨了涵盖分布式一致性优化、可分资源分配以及非合作博弈中纳什均衡求解的分布式决策算法,重点分析了关键技术与典型方法。最后,展望该领域未来发展方向与前景。

关键词:关键词:开放多智能体系统;时变拓扑;分布式控制;分布式优化;纳什均衡搜索

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

Reference

[1]Abdelrahim M, Hendrickx JM, Heemels WPMH, 2017. Max-consensus in open multi-agent systems with gossip interactions. IEEE 56th Annual Conf on Decision and Control, p.4753-4758.

[2]Chen ST, Wan Y, Cao JD, 2025. Adaptive Nash equilibrium seeking in hybrid heterogeneous open multi-agent systems under DoS attacks. IEEE Trans Netw Sci Eng, 12(4):2770-2782.

[3]Dashti ZAZS, Seatzu C, Franceschelli M, 2019. Dynamic consensus on the median value in open multi-agent systems. IEEE 58th Conf on Decision and Control, p.3691-3697.

[4]Dashti ZAZS, Oliva G, Seatzu C, et al., 2022. Distributed mode computation in open multi-agent systems. IEEE Contr Syst Lett, 6:3481-3486.

[5]de Galland CM, Hendrickx JM, 2023. Fundamental performance limitations for average consensus in open multi-agent systems. IEEE Trans Automat Contr, 68(2):646-659.

[6]de Galland CM, Martin S, Hendrickx JM, 2020. Open multi-agent systems with variable size: the case of gossiping. https://arxiv.org/abs/2009.02970v1

[7]de Galland CM, Vizuete R, Hendrickx JM, et al., 2021. Random coordinate descent algorithm for open multi-agent systems with complete topology and homogeneous agents. 60th IEEE Conf on Decision and Control, p.1701-1708.

[8]de Galland CM, Vizuete R, Hendrickx JM, et al., 2024. Random coordinate descent for resource allocation in open multiagent systems. IEEE Trans Automat Contr, 69(11):7600-7613.

[9]Deplano D, Bastianello N, Franceschelli M, et al., 2026. Optimization and learning in open multi-agent systems. IEEE Trans Automat Contr, 71(6):3864-3879.

[10]Dong JW, Yassine A, Armitage A, et al., 2023. Multi-agent reinforcement learning for intelligent V2G integration in future transportation systems. IEEE Trans Intell Transport Syst, 24(12):15974-15983.

[11]Dong LJ, Nguang SK, 2021. Relay tracking controller design for multiagent systems with varying number of agents. IEEE Trans Syst Man Cybern Syst, 51(10):6147-6158.

[12]Dutta A, Doan TT, 2025. Distributed optimization in open networks under redundancy. American Control Conf, p.2185-2190.

[13]Fang X, Wen GH, Huang TW, et al., 2022a. Distributed Nash equilibrium seeking over Markovian switching communication networks. IEEE Trans Cybern, 52(6):5343-5355.

[14]Fang X, Zhou JL, Wen GH, 2022b. Location game of multiple unmanned surface vessels with quantized communications. IEEE Trans Circ Syst, 69(3):1322-1326.

[15]Franceschelli M, Frasca P, 2018. Proportional dynamic consensus in open multi-agent systems. IEEE Conf on Decision and Control, p.900-905.

[16]Franceschelli M, Frasca P, 2021. Stability of open multiagent systems and applications to dynamic consensus. IEEE Trans Automat Contr, 66(5):2326-2331.

[17]Hayashi N, 2023. Distributed subgradient method in open multiagent systems. IEEE Trans Automat Contr, 68(10):6192-6199.

[18]Hendrickx JM, Martin S, 2017. Open multi-agent systems: gossiping with random arrivals and departures. IEEE 56th Annual Conf on Decision and Control, p.763-768.

[19]Hendrickx JM, Rabbat MG, 2020. Stability of decentralized gradient descent in open multi-agent systems. 59th IEEE Conf on Decision and Control, p.4885-4890.

[20]Hsieh YG, Iutzeler F, Malick J, et al., 2021. Optimization in open networks via dual averaging. 60th IEEE Conf on Decision and Control, p.514-520.

[21]Huynh TD, Jennings NR, Shadbolt NR, 2006. An integrated trust and reputation model for open multi-agent systems. Auton Agent Multi-Agent Syst, 13(2):119-154.

[22]Jia ZA, Chi M, Liu ZW, et al., 2026a. Consensus analysis and convergence rate optimization for open multiagent systems. IEEE Trans Cybern, 56(3):1191-1201.

[23]Jia ZA, Chi M, Liu ZW, et al., 2026b. Nondisruptive consensus in discrete-time open multiagent systems with double integrator dynamics. IEEE Trans Automat Contr, 71(3):2093-2100.

[24]Li CC, Dong LJ, Nguang SK, 2017. Cooperative control of multi-agent systems with variable number of tracking agents. IET Contr Theory Appl, 11(12):1922-1927.

[25]Li XD, Wen GH, 2026. Adaptive output consensus of heterogeneous open multi-agent systems under actuator attacks. Acta Electr Sin, 54(2):1698-12709 (in Chinese).

[26]Li XD, Lv YZ, Wen GH, et al., 2023. Tracking consensus of multi-agent systems with varying number of agents under actuator attacks. IEEE Trans Circ Syst II Expr Briefs, 70(12):4514-4518.

[27]Li XD, Wen GH, Sun CY, 2024. Leader-following consensus of open multi-unmanned aerial vehicles systems under directed topology. SICE Festival with Annual Conf, p.752-757.

[28]Li XD, Lv YZ, Yang T, et al., 2026a. Fully distributed adaptive tracking consensus of open multi-agent systems. IFAC World Congress, in press.

[29]Li XD, Wen GH, Lv YZ, et al., 2026b. Output consensus tracking of heterogeneous open multiagent systems under actuator attacks. IEEE Trans Contr Netw Syst, 13(1):423-435.

[30]Liu WT, Lei JL, Yi P, et al., 2026. Online best-response algorithm in open non-cooperative games. IEEE Trans Automat Contr, 71(5):3247-3262.

[31]Liu XM, Wang ZP, Zhang H, et al., 2024. Fuzzy cooperative output regulation for open nonlinear multiagent systems. IEEE Trans Fuzzy Syst, 32(6):3693-3702.

[32]Liu YX, Ye MJ, Ding L, et al., 2024. Distributed online resource allocation in open networks. IEEE Trans Automat Contr, 69(12):8876-8883.

[33]Liu YX, Ye MJ, Ding L, et al., 2025. Distributed strategy design for free-in and free-out aggregative games. IEEE Trans Automat Contr, 70(8):5592-5599.

[34]Liu YX, Ye MJ, Ding L, et al., 2026a. Algorithm design and regret analysis for aggregative optimization in open multi-agent systems. IEEE Trans Automat Contr, early access.

[35]Liu YX, Ye MJ, Ding L, et al., 2026b. Distributed Nash equilibrium seeking with a dynamic set of players. Automatica, 183:112598.

[36]Liu YX, Ye MJ, Ding L, et al., 2026c. Distributed online optimization over partially free-in and free-out networks. IEEE Trans Automat Contr, early access.

[37]Luan M, Wen GH, Lv YZ, et al., 2024. Distributed constrained optimization over unbalanced time-varying digraphs: a randomized constraint solving algorithm. IEEE Trans Automat Contr, 69(8):5154-5167.

[38]Luan M, Wen GH, Ge XH, et al., 2025. Reputation-based optimization for distributed energy management under persistent DoS attacks. IEEE Trans Ind Inform, 21(2):1220-1229.

[39]Makridis E, Oliva G, Charalambous T, 2025. Multi-cluster distributed optimization in open multi-agent systems over directed graphs with acknowledgement messages. IEEE 64th Conf on Decision and Control, p.6820-6825.

[40]Olfati-Saber R, Fax JA, Murray RM, 2007. Consensus and cooperation in networked multi-agent systems. Proc IEEE, 95(1):215-233.

[41]Qu GN, Wierman A, Li N, 2022. Scalable reinforcement learning for multiagent networked systems. Oper Res, 70(6):3601-3628.

[42]Restrepo E, Loria A, Sarras I, et al., 2022. Consensus of open multi-agent systems over dynamic undirected graphs with preserved connectivity and collision avoidance. IEEE 61st Conf on Decision and Control, p.4609-4614.

[43]Restrepo E, Secchi C, Robuffo Giordano P, 2025. Passivity preserving energy-aware design for multi-dimensional switched systems: application to open multi-robot systems. Automatica, 181:112496.

[44]Riverso S, Farina M, Ferrari-Trecate G, 2013. Plug-and-play decentralized model predictive control for linear systems. IEEE Trans Automat Contr, 58(10):2608-2614.

[45]Riverso S, Sarzo F, Ferrari-Trecate G, 2015. Plug-and-play voltage and frequency control of islanded microgrids with meshed topology. IEEE Trans Smart Grid, 6(3):1176-1184.

[46]Sawamura R, Hayashi N, Inuiguchi M, 2025. A distributed primal-dual push-sum algorithm on open multiagent networks. IEEE Trans Automat Contr, 70(2):1192-1199.

[47]Sebastián E, Duong T, Atanasov N, et al., 2025. Physics-informed multiagent reinforcement learning for distributed multirobot problems. IEEE Trans Robot, 41:4499-4517.

[48]Shi P, Yan B, 2021. A survey on intelligent control for multiagent systems. IEEE Trans Syst Man Cybern Syst, 51(1):161-175.

[49]Vizuete R, de Galland CM, Hendrickx JM, et al., 2022. Resource allocation in open multi-agent systems: an online optimization analysis. IEEE 61st Conf on Decision and Control, p.5185-5191.

[50]Wen GH, Duan ZS, Chen GR, et al., 2014. Consensus tracking of multi-agent systems with Lipschitz-type node dynamics and switching topologies. IEEE Trans Circ Syst I Regul Pap, 61(2):499-511.

[51]Wen GH, Fang X, Zhou J, et al., 2022. Robust formation tracking of multiple autonomous surface vessels with individual objectives: a non-cooperative game-based approach. Contr Eng Pract, 119:104975.

[52]Wen GH, Fang X, Luan M, 2025. An overview of distributed Nash equilibrium seeking in non-cooperative games for multi-agent systems: a dynamic control-based perspective. Artif Intell Sci Eng, 1(4):239-254.

[53]Wu XY, Lin J, Jiang SC, et al., 2026. Open distributed convex optimization. IEEE Trans Automat Contr, 71(2):1302-1309.

[54]Xu JZ, Liu ZW, He DX, et al., 2025. Dynamic Nash equilibrium seeking for constrained non-cooperative game of open multiagent systems. IEEE Trans Syst Man Cybern Syst, 55(6):3846-3855.

[55]Xue MQ, Tang Y, Ren W, et al., 2022. Stability of multi-dimensional switched systems with an application to open multi-agent systems. Automatica, 146:110644.

[56]Yang T, Shi XH, Zeng QH, et al., 2025. Optimization methods in fully cooperative scenarios: a review of multiagent reinforcement learning. Front Inform Technol Electron Eng, 26(4):479-509.

[57]Zhu HT, Lu JQ, Lou YJ, et al., 2025. Distributed saturated impulsive quasi-consensus for leader-follower multi-agent systems: an open topology framework. IEEE/CAA J Automat Sin, 12(9):1941-1943.

Open peer comments: Debate/Discuss/Question/Opinion

<1>

Please provide your name, email address and a comment





Full Text:   <1>

CLC number: TP13

On-line Access: 2026-07-29

Received: 2026-05-01

Revision Accepted: 2026-06-01

Crosschecked: 2026-07-29

Cited: 0

Clicked: 3

Citations:  Bibtex RefMan EndNote GB/T7714

 ORCID:

Guanghui WEN

0000-0003-0070-8597

Meng LUAN

0009-0008-7170-3287

Xiao FANG

0000-0003-2050-9894

Xiaodong LI

0000-0003-3561-9762

Journal of Zhejiang University-SCIENCE, 38 Zheda Road, Hangzhou 310027, China
Tel: +86-571-87952783; E-mail: cjzhang@zju.edu.cn
Copyright © 2000 - 2026 Journal of Zhejiang University-SCIENCE