广东工业大学学报 ›› 2024, Vol. 41 ›› Issue (04): 52-60.doi: 10.12052/gdutxb.230084

• 控制科学与工程 • 上一篇    

动态事件触发脉冲机制下多智能体系统的拟一致性

陈应瑟, 彭世国, 王永华   

  1. 广东工业大学 自动化学院, 广东 广州 510006
  • 收稿日期:2023-07-03 发布日期:2024-08-13
  • 通信作者: 彭世国(1967–),男,教授,博士生导师,主要研究方向为复杂系统随机控制理论,E-mail:psg7202@126.com
  • 作者简介:陈应瑟(1998–),男,硕士研究生,主要研究方向为多智能体系统一致性问题、脉冲控制、事件触发控制,E-mail:635720012@qq.com
  • 基金资助:
    国家自然科学基金资助面上项目(61973092) ;广东省自然科学基金资助项目(2019A1515012104)

Quasi-consensus of Multi-agent System under Dynamic Event-triggered Impulsive Mechanism

Chen Ying-se, Peng Shi-guo, Wang Yong-hua   

  1. School of Automation, Guangdong University of Technology, Guangzhou 510006, China
  • Received:2023-07-03 Published:2024-08-13

摘要: 考虑到多智能体系统在复杂环境中受到外部干扰和控制器受到恶意攻击等问题,本文旨在研究一类受扰非线性多智能体系统在事件触发机制和脉冲控制下的领导跟随拟一致性。不同于现有文献中需要事先指定最小事件触发间隔,本文所设计的动态事件触发机制通过其参数的选择加以保证,从而避免了芝诺行为的发生。基于此机制,进一步给出了系统实现拟一致性的一些充分条件以及相应误差上界的估量值。最后,通过数值仿真例子验证了结果的有效性。

关键词: 动态事件触发机制, 多智能体系统, 拟一致性, 脉冲控制, 外部干扰

Abstract: Considering the situation that multi-agent systems subject to an external disturbance in the complex environment and malicious attacks in the controller, the leader-following quasi-consensus of a class of disturbed nonlinear multi-agent system under the impulsive control and an event-triggered mechanism is studied. Unlike existing literature that requires a minimum triggering interval to be specified in advance, Zeno behavior is avoided by setting appropriate parameters in the designed dynamic event-triggered mechanism. Based on this mechanism, some sufficient conditions to achieve quasi-consensus are further proposed, and the upper bound of error states is also estimated. Finally, a numerical simulation example verifies the feasibility of the results.

Key words: dynamic event-triggered mechanism, multi-agent systems, quasi-consensus, impulsive control, external disturbances

中图分类号: 

  • TP273
[1] NUNO E, LORIA A, PANTELEY E. Leaderless consensus formation control of cooperative multi-agent vehicles without velocity measurements[J]. IEEE Control Systems Letters, 2021, 6: 902-907.
[2] YU W, GOU J Z, HU X T, et al. A new consensus theory-based method for formation control and obstacle avoidance of UAVs[J]. Aerospace Science and Technology, 2020, 107: 106332.
[3] ZHOU Q, LI Y G, NIU Y T. Intelligent anti-jamming communication for wireless sensor networks: a multi-agent reinforcement learning approach[J]. IEEE Open Journal of the Communications Society, 2021, 2: 775-784.
[4] 曾梓贤, 彭世国, 黄昱嘉, 等. 两种不同脉冲欺骗攻击下随机多智能体系统的均方拟一致性[J]. 广东工业大学学报, 2022, 39(1): 71-77.
ZENG Z X, PENG S G, HUANG Y J, et al. Mean square quasi-consensus of stochastic multi-agent systems under two different impulsive deception attacks[J]. Journal of Guangdong University of Technology, 2022, 39(1): 71-77.
[5] GUAN Z H, LIU Z W, FENG G, et al. Impulsive consensus algorithms for second-order multi-agent networks with sampled information[J]. Automatica, 2012, 48(7): 1397-1404.
[6] MA T D, ZHANG Z L, CUI B. Impulsive consensus of nonlinear fuzzy multi-agent systems under dos attack[J]. Nonlinear Analysis: Hybrid Systems, 2022, 44: 101155.
[7] YANG X Y, PEMG D X, LV X X, et al. Recent progress in impulsive control systems[J]. Mathematics and Computers in Simulation, 2019, 155: 244-268.
[8] DING L, HAN Q L, GE X, et al. An overview of recent advances in event-triggered consensus of multi-agent systems[J]. IEEE Transactions on Cybernetics, 2017, 48(4): 1110-1123.
[9] 谷志华, 彭世国, 黄昱嘉, 等. 基于事件触发脉冲控制的具有ROUs和RONs的非线性多智能体系统的领导跟随一致性研究[J]. 广东工业大学学报, 2023, 40(1): 50-55.
GU Z H, PENG S G, HUANG Y J, et al. Leader-following consensus of nonlinear multi-agent systems with ROUs and RONs via event-triggered impulsive control[J]. Journal of Guangdong University of Technology, 2023, 40(1): 50-55.
[10] GU Z H, PENG S G, HUANG Y J. Quasi-consensus of disturbed nonlinear multi-agent systems with event-triggered impulsive control[J]. Applied Sciences, 2022, 12(15): 7580.
[11] ZHUANG J W, PENG S G, WANG Y H. Event-triggered intermittent-based impulsive control for stabilization of nonlinear systems[J]. IEEE Transactions on Circuits and Systems II: Express Briefs, 2022, 69(12): 5039-5043.
[12] GIRARD A. Dynamic triggering mechanisms for event-triggered control[J]. IEEE Transactions on Automatic Control, 2014, 60(7): 1992-1997.
[13] YI X L, LIU K, DIMAROGONAS D V, et al. Dynamic eventtriggered and self-triggered control for multi-agent systems[J]. IEEE Transactions on Automatic Control, 2018, 64(8): 3300-3307.
[14] GUO H H, LIU J, AHN C K, et al. Dynamic event-triggered impulsive control for stochastic nonlinear systems with extension in complex networks[J]. IEEE Transactions on Circuits and Systems I: Regular Papers, 2022, 69(5): 2167-2178.
[15] AI Z D, PENG L H, ZONG G D, et al. Impulsive control for nonlinear systems under dos attacks: a dynamic event-triggered method[J]. IEEE Transactions on Circuits and Systems II: Express Briefs, 2022, 69(9): 3839-3843.
[16] HE W L, GAO X Y, ZHONG W M, et al. Secure impulsive synchronization control of multi-agent systems under deception attacks[J]. Information Sciences, 2018, 459: 354-368.
[17] ZHU H T, LU J Q, LOU J G. Event-triggered impulsive control for nonlinear systems: the control packet loss case[J]. IEEE Transactions on Circuits and Systems II: Express Briefs, 2022, 69(7): 3204-3208.
[18] LI X D, LI P. Input-to-state stability of nonlinear systems: eventtriggered impulsive control[J]. IEEE Transactions on Automatic Control, 2021, 67(3): 1460-1465.
[19] YANG N, ZHANG S, SU H. Event-triggered impulsive control for stability of stochastic delayed complex networks under deception attacks[J]. Engineering Applications of Artificial Intelligence, 2023, 121: 105953.
[20] HUO S C, WU H, ZHANG Y. Secure consensus control for multi-agent systems against attacks on actuators and sensors[J]. International Journal of Robust and Nonlinear Control, 2022, 32(8): 4861-4877.
[21] HU Z H, MU X W. Event-triggered impulsive control for stochastic networked control systems under cyber attacks[J]. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2021, 52(9): 5636-5645.
[22] WANG Z D, HO D W C, LIU X H. Variance-constrained filtering for uncertain stochastic systems with missing measurements[J]. IEEE Transactions on Automatic Control, 2003, 48(7): 1254-1258.
[23] HUANG L R, MAO X R. Robust delayed-state-feedback stabilization of uncertain stochastic systems[J]. Automatica, 2009, 45(5): 1332-1339.
[1] 谢光强, 万梓坤, 李杨. 基于分层邻域选择的切换拓扑多智能体系统一致性协议[J]. 广东工业大学学报, 2024, 41(04): 44-51.
[2] 胡然, 彭世国. 基于脉冲观测器的多智能体系统的领导跟随一致性[J]. 广东工业大学学报, 2023, 40(05): 88-93.
[3] 谷志华, 彭世国, 黄昱嘉, 冯万典, 曾梓贤. 基于事件触发脉冲控制的具有ROUs和RONs的非线性多智能体系统的领导跟随一致性研究[J]. 广东工业大学学报, 2023, 40(01): 50-55.
[4] 谢光强, 许浩然, 李杨, 陈广福. 基于多智能体强化学习的社交网络舆情增强一致性方法[J]. 广东工业大学学报, 2022, 39(06): 36-43.
[5] 曲燊, 车伟伟. FDI攻击下非线性多智能体系统分布式无模型自适应控制[J]. 广东工业大学学报, 2022, 39(05): 75-82.
[6] 刘建华, 李佳慧, 刘小斌, 穆树娟, 董宏丽. 事件触发机制下的多速率多智能体系统非脆弱一致性控制[J]. 广东工业大学学报, 2022, 39(05): 102-111.
[7] 曾梓贤, 彭世国, 黄昱嘉, 谷志华, 冯万典. 两种不同脉冲欺骗攻击下随机多智能体系统的均方拟一致性[J]. 广东工业大学学报, 2022, 39(01): 71-77.
[8] 谢光强, 赵俊伟, 李杨, 许浩然. 基于多集群系统的车辆协同换道控制[J]. 广东工业大学学报, 2021, 38(05): 1-9.
[9] 郑子钊, 彭世国, 付志文, 徐云剑. 脉冲控制下一类多权重复杂网络的鲁棒H同步[J]. 广东工业大学学报, 2021, 38(03): 55-61.
[10] 张弘烨, 彭世国. 基于模型简化法的含有随机时延的多智能体系统一致性研究[J]. 广东工业大学学报, 2019, 36(02): 86-90,96.
[11] 张振华, 彭世国. 二阶多智能体系统拓扑切换下的领导跟随一致性[J]. 广东工业大学学报, 2018, 35(02): 75-80.
[12] 罗贺富, 彭世国. 多时变时滞的多智能体系统的分布式编队控制[J]. 广东工业大学学报, 2017, 34(04): 89-96.
[13] 唐平; 杨宜民;. 多智能体系统与足球机器人系统体系结构研究[J]. 广东工业大学学报, 2001, 18(4): 1-4.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!