广东工业大学学报 ›› 2024, Vol. 41 ›› Issue (04): 44-51.doi: 10.12052/gdutxb.230034

• 控制科学与工程 • 上一篇    

基于分层邻域选择的切换拓扑多智能体系统一致性协议

谢光强, 万梓坤, 李杨   

  1. 广东工业大学 计算机学院, 广东 广州 510006
  • 收稿日期:2023-02-24 发布日期:2024-08-13
  • 通信作者: 李杨(1980–),女,教授,博士,主要研究方向为多智能体、差分隐私保护,E-mail:liyang@gdut.edu.cn
  • 作者简介:谢光强(1979–),男,教授,博士,主要研究方向为多智能体、智能控制、差分隐私保护,E-mail:xiegq@gdut.edu.cn
  • 基金资助:
    国家自然科学基金资助项目(62006047,618760439)

Consensus of Switched Topology in Multi-agent System Based on Layered Neighbor Selection

Xie Guang-qiang, Wan Zi-kun, Li Yang   

  1. School of Computer Science and Technology, Guangdong University of Technology, Guangzhou 510006, China
  • Received:2023-02-24 Published:2024-08-13

摘要: 在切换拓扑的多智能体系统中,针对高低密度信息减弱一致性的问题,提出了一种基于分层邻域选择算法(Layered Neighbor Selection, LNS) ,该算法对智能体的邻域进行层次划分,从每层中选取具有代表性的邻居智能体进行通信、状态更新和状态演化,然后设计了层次调整策略和层次融合策略来加快收敛速度,最后设计了分层邻域选择一致性协议,且给出了层数对收敛的影响。现有的一致性协议,收敛效果受限于特定密度范围,受不同密度的影响较大,而本文提出的协议能适应不同密度范围,并在系统稳定的条件下提升收敛速度,且通过李雅普诺夫函数法证明了一致性协议的稳定性。最后通过仿真实验,并与几种一致性协议进行比较,验证了所设计的一致性协议能有效加快系统收敛速度。

关键词: 多智能体系统, 一致性, 切换拓扑, 分层邻域选择, 李雅普诺夫函数

Abstract: In the multi-agent systems with switching topology, to address the consistency problem weaken by the high and low density information, a Layered Neighbor Selection(LNS) algorithm is proposed. First, this algorithm divides the neighborhood of the agent into layers, and selects the representative neighbor agents from each layer for communication, state update and state evolution. Then, it designs a layer adjustment strategy and a layer fusion strategy to accelerate the convergence speed, Finally, it designs a layer neighborhood selection consistency protocol, and provides the influence of the number of layers on the convergence. The convergence effect of the traditional consistency protocol is limited to a specific density range and is greatly affected by different densities. Differently, the proposed protocol of this paper can adapt to different density ranges and improve the convergence speed under the condition of system stability. The stability of the consistency protocol is proved by the Lyapunov function method. The effectiveness of the proposed consistency protocol is verified by simulation in comparison with several consistency protocols.

Key words: multi-agent system, consensus, switching topology, layered neighbor selection, Lyapunov function

中图分类号: 

  • TP391
[1] AMIRKHANI A, BARSHOOI A H. Consensus in multi-agent systems: a review[J]. Artificial Intelligence Review, 2021, 55(5): 1-39.
[2] CAO X, ZHANG C, ZHAO D, et al. Guaranteed cost positive consensus for multi-agent systems with multiple time-varying delays and MDADT switching[J]. Nonlinear Dynamics, 2022, 107(4): 3557-3572.
[3] 孙彧, 曹雷, 陈希亮, 等. 多智能体深度强化学习研究综述[J]. 计算机工程与应用, 2020, 56(5): 13-24.
SUN Y, CAO L, CHEN X L, et al. Overview of multi-agent deep reinforcement learning[J]. Computer Engineering and Applications, 2020, 56(5): 13-24.
[4] LIU C, JIANG B, ZHANG K, et al. Distributed fault-tolerant consensus tracking control of multi-agent systems under fixed and switching topologies[J]. IEEE Transactions on Circuits and Systems I: Regular Papers, 2021, 68(4): 1646-1658.
[5] DAS A, GERVET T, ROMOFF J, et al. Tarmac: targeted multi-agent communication[C]//International Conference on Machine Learning. LA: PMLR, 2019: 1538-1546.
[6] KASHYAP N, YANG C W, SIERLA S, et al. Automated fault location and isolation in distribution grids with distributed control and unreliable communication[J]. IEEE Transactions on Industrial Electronics, 2014, 62(4): 2612-2619.
[7] DAVOODI M R, KHORASANI K, TALEBI H A, et al. Distributed fault detection and isolation filter design for a network of heterogeneous multiagent systems[J]. IEEE Transactions on Control Systems Technology, 2013, 22(3): 1061-1069.
[8] DAVOODI M, MESKIN N, KHORASANI K. Simultaneous fault detection and consensus control design for a network of multi-agent systems[J]. Automatica, 2016, 66: 185-194.
[9] PALAU A S, DHADA M H, BAKLIWAL K, et al. An industrial multi agent system for real-time distributed collaborative prognostics[J]. Engineering Applications of Artificial Intelligence, 2019, 85: 590-606.
[10] LI Y, TANG C, LI K, et al. Consensus-based cooperative control for multi-platoon under the connected vehicles environment[J]. IEEE Transactions on Intelligent Transportation Systems, 2018, 20(6): 2220-2229.
[11] LI Y, CHEN W, PEETA S, et al. Platoon control of connected multi-vehicle systems under V2X communications: design and experiments[J]. IEEE Transactions on Intelligent Transportation Systems, 2019, 21(5): 1891-1902.
[12] RUAN X, FENG J, XU C, et al. Observer-based dynamic event-triggered strategies for leader-following consensus of multi-agent systems with disturbances[J]. IEEE Transactions on Network Science and Engineering, 2020, 7(4): 3148-3158.
[13] JIANG F, XIE D, CAO M. Dynamic consensus of double-integrator multi-agent systems with aperiodic impulsive protocol and time-varying delays[J]. IET Control Theory & Applications, 2017, 11(16): 2879-2885.
[14] YI X, LIU K, DIMAROGONAS D V, et al. Dynamic event-triggered and self-triggered control for multi-agent systems[J]. IEEE Transactions on Automatic Control, 2018, 64(8): 3300-3307.
[15] OLFATI-SABER R, MURRAY R M. Consensus problems in networks of agents with switching topology and time-delays[J]. IEEE Transactions on Automatic Control, 2004, 49(9): 1520-1533.
[16] SABER R O, MURRAY R M. Consensus protocols for networks of dynamic agents[C]//Proceedings of the 2003 American Control Conference. Toronto: IEEE, 2003: 951-956.
[17] MOREAU L. Stability of multiagent systems with time-dependentcommunication links[J]. IEEE Transactions on Automatic Control, 2005, 50(2): 169-182.
[18] KHATERI K, POURGHOLI M, MONTAZERI M, et al. A comparison between decentralized local and global methods for connectivity maintenance of multi-robot networks[J]. IEEE Robotics and Automation Letters, 2019, 4(2): 633-640.
[19] KAN Z, YUCELEN T, DOUCETTE E, et al. A finite-time consensus framework over time-varying graph topologies with temporal constraints[J]. Journal of Dynamic Systems, Measurement, and Control, 2017, 139(7): 071012.
[20] 郑军, 颜文俊. 多主体汇聚问题离散算法的稳定性[J]. 浙江大学学报: 工学版, 2007, 41(10): 1684-1687.
ZHENG J, YAN W J. Sufficient condition on stability of multi-agent rendezvous discrete algorithm[J]. Journal of Zhejiang University (Engineering Science) , 2007, 41(10): 1684-1687.
[21] LU M, WU J, ZHAN X, et al. Consensus of second-order heterogeneous multi-agent systems with and without input saturation[J]. ISA Transactions, 2022, 126: 14-20.
[22] GOMEZ M A, RAMIREZ A. A scalable approach for consensus stability analysis of a large-scale multi-agent system with single delay[J]. IEEE Transactions on Automatic Control, 2022, 68(7): 4375-4382.
[23] JADBABIE A, LIN J, MORSE A S. Coordination of groups of mobile autonomous agents using nearest neighbor rules[J]. IEEE Transactions on Automatic Control, 2003, 48(6): 988-1001.
[24] MASOUD A A. Nearest neighbor-based rendezvous for sparsely connected mobile agents[J]. Journal of Dynamic Systems, Measurement, and Control, 2015, 137(12): 121002.
[25] XIE G, XU H, LI Y, et al. Fast distributed consensus seeking in large-scale and high-density multi-agent systems with connectivity maintenance[J]. Information Sciences, 2022, 608: 1010-1028.
[26] CHURCHMAN A. Disentangling the concept of density[J]. Journal of Planning Literature, 1999, 13(4): 389-411.
[27] HORN R A, JOHNSON C R. Matrix analysis[M]. London: Cambridge University Press, 2012.
[28] CHEN L, DUAN H, ZENG Z. Fast convergent tracking control of networked UAVs over a dynamic interaction topology[J]. IEEE Transactions on Circuits and Systems II: Express Briefs, 2022, 69(12): 4884-4888.
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