广东工业大学学报 ›› 2022, Vol. 39 ›› Issue (05): 102-111.doi: 10.12052/gdutxb.220066

• • 上一篇    下一篇

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

刘建华1,3,4, 李佳慧1,3,4, 刘小斌2, 穆树娟1,3,4, 董宏丽1,3,4   

  1. 1. 东北石油大学 人工智能能源研究院,黑龙江 大庆 163318;
    2. 上海电子信息职业技术学院 中德工程学院,上海 201411;
    3. 东北石油大学 黑龙江省网络化与智能控制重点实验室,黑龙江 大庆 163318;
    4. 东北石油大学 三亚海洋油气研究院,海南 三亚 572024
  • 收稿日期:2022-03-24 发布日期:2022-07-18
  • 通信作者: 刘小斌(1979–),女,副教授,主要研究方向为电机控制技术、电力拖动自动控制系统等,E-mail:68486201@qq.com
  • 作者简介:刘建华(1996–),男,硕士研究生,主要研究方向为多智能体的一致性控制等
  • 基金资助:
    国家自然科学基金资助项目(U21A2019,61873058,61933007);海南省科技专项资助项目(ZDYF2022SHFZ105);黑龙江省博士后面上项目(LBH-Z21123);东北石油大学人才引进科研启动经费资助项目(2021KQ14)

Event-Triggered Mechanism Based Non-Fragile Consensus Control for Multi-Rate Multi-Agent Systems

Liu Jian-hua1,3,4, Li Jia-hui1,3,4, Liu Xiao-bin2, Mu Shu-juan1,3,4, Dong Hong-li1,3,4   

  1. 1. Artificial Intelligence Energy Research Institute, Northeast Petroleum University, Daqing 163318, China;
    2. Sino German Institute of Sngineering, Shanghai Technical Institute of Electronics & Information, Shanghai 201411, China;
    3. Heilongjiang Provincial Key Laboratory of Networking and Intelligent Control, Northeast Petroleum University, Daqing 163318, China;
    4. Sanya Offshore Oil & Gas Research Institute, Northeast Petroleum University, Sanya 572024, China
  • Received:2022-03-24 Published:2022-07-18

摘要: 针对一类多速率多智能体系统,研究在事件触发机制下的非脆弱H一致性控制问题。为了更符合实际需要,采用多速率采样策略,并通过提升技术将多速率采样转化为单速率采样。考虑到智能体间的通信负担,引入事件触发机制来减少智能体间的通信次数。此外,考虑到控制器在执行过程中可能出现的不精确性,本文设计一种可以容忍执行过程中变化/波动的控制器。综上,本文的目的是设计一种基于观测器的事件触发非脆弱控制器来实现多智能体系统的H一致性控制。利用线性矩阵不等式技术,得到使系统满足H一致性控制的充分条件,然后设计控制器参数。最后,为了说明事件触发控制方法的有效性,给出一个数值仿真实例。

关键词: 多智能体系统, 多速率采样, 事件触发机制, 非脆弱控制器

Abstract: The research on the non-fragile H-consensus control problem for a class of multi-rate multi-agent system under the event-triggered (ET) mechanism is focused on. In order to be more in line with actual need, a multi-rate sampling strategy is adopted, which leads to a multi-rate sampling that can be converted into a single-rate sampling via lifting technique. Considering the transmission burden among the agents, an ET mechanism is introduced to reduce the numbers of transmission among the agents. In addition, in view of the possible inaccuracy of the controller implementation, a controller that can tolerate the changes/fluctuations during the implementation is designed to make the multi-agent system more robust. An observer-based ET non-fragile controller is designed to achieve the H-consensus control of the multi-agent system, in which the controller can tolerate the variations/fluctuations during the implementation. By using the linear matrix inequality technique, the sufficient conditions are obtained that can ensure the H-consensus control of the considered system, and then the controller parameters are designed. Finally, a numerical simulation example is given to prove the effectiveness of the ET control method.

Key words: multi-agent system, multi-rate sampling, event-triggered mechanism, non-fragile controller

中图分类号: 

  • TP273
[1] DU H, WEN G, WU D, et al. Distributed fixed-time consensus for nonlinear heterogeneous multi-agent systems [J]. Automatica, 2020, 113: 108797.
[2] ZOU W, SHI P, XIANG Z, et al. Finite-time consensus of second-order switched nonlinear multi-agent systems [J]. IEEE Transactions on Neural Networks and Learning Systems, 2019, 31(5): 1757-1762.
[3] DUNBAR W B. Distributed receding horizon control of dynamically coupled nonlinear systems [J]. IEEE Transactions on Automatic Control, 2007, 52(7): 1249-1263.
[4] FRANCO E, MAGNI L, PARISINI T, et al. Cooperative constrained control of distributed agents with nonlinear dynamics and delayed information exchange: a stabilizing receding-horizon approach [J]. IEEE Transactions on Automatic Control, 2008, 53(1): 324-338.
[5] LIN P, JIA Y, LI L. Distributed robust H consensus control in directed networks of agents with time-delay [J]. Systems & Control Letters, 2008, 57(8): 643-653.
[6] WANG J, DUAN Z, WEN G, et al. Distributed robust control of uncertain linear multi-agent systems [J]. International Journal of Robust and Nonlinear Control, 2015, 25(13): 2162-2179.
[7] XU W, WANG Z, HO D W C. Finite-horizon H consensus for multiagent systems with redundant channels via an observer-type event-triggered scheme [J]. IEEE Transactions on Cybernetics, 2017, 48(5): 1567-1576.
[8] ZHAO Y, DUAN Z, WEN G, et al. Distributed H consensus of multi-agent systems: a performance region-based approach [J]. International Journal of Control, 2012, 85(3): 332-341.
[9] SAKTHIVEL R, KANAKALAKSHMI S, KAVIARASAN B, et al. Finite-time consensus of input delayed multi-agent systems via non-fragile controller subject to switching topology [J]. Neurocomputing, 2019, 325: 225-233.
[10] BAO H, PARK J H, CAO J. Non-fragile state estimation for fractional-order delayed memristive BAM neural networks [J]. Neural Networks, 2019, 119: 190-199.
[11] JIANG X, XIA G, FENG Z. Non-fragile consensus control for singular multi-agent systems with Lipschitz nonlinear dynamics [J]. Neurocomputing, 2019, 351: 123-133.
[12] WANG Z, DING D, SHU H. Non-fragile H control with randomly occurring gain variations, distributed delays and channel fadings [J]. IET Control Theory & Applications, 2015, 9(2): 222-231.
[13] MA L, WANG Z, LAM H K. Mean-square H consensus control for a class of nonlinear time-varying stochastic multiagent systems: the finite-horizon case [J]. IEEE Transactions on Systems, Man, and Cybernetics:Systems, 2016, 47(7): 1050-1060.
[14] BU X, DONG H, WANG Z, et al. Non-fragile distributed fault estimation for a class of nonlinear time-varying systems over sensor networks: the finite-horizon case [J]. IEEE Transactions on Signal and Information Processing over Networks, 2018, 5(1): 61-69.
[15] LI J, DONG H, LIU H, et al. Sampled-data non-fragile state estimation for delayed genetic regulatory networks under stochastically switching sampling periods [J]. Neurocomputing, 2021, 463: 168-176.
[16] LIU L, MA L, ZHANG J, et al. Distributed non-fragile set-membership filtering for nonlinear systems under fading channels and bias injection attacks [J]. International Journal of Systems Science, 2021, 52(6): 1192-1205.
[17] GENG H, LIANG Y, LIU Y, et al. Bias estimation for asynchronous multi-rate multi-sensor fusion with unknown inputs [J]. Information Fusion, 2018, 39: 139-153.
[18] QU B, WANG Z, SHEN B. Fusion estimation for a class of multi-rate power systems with randomly occurring SCADA measurement delays [J]. Automatica, 2021, 125: 109408.
[19] HUA C, GE C, GUAN X. Synchronization of chaotic Lur’e systems with time delays using sampled-data control [J]. IEEE Transactions on Neural Networks and Learning Systems, 2014, 26(6): 1214-1221.
[20] SHEN Y, WANG Z, DONG H, et al. Multi-sensor multi-rate fusion estimation for networked systems: advances and perspectives[J]. Information Fusion, 2022, 82: 19-27.
[21] 马伟伟, 贾新春, 张大伟. 双率采样系统的基于观测器的网络化H控制[J]. 自动化学报, 2015, 41(10): 1788-1797.
MA W W, JIA X C, ZHANG D W. Observer-based networked H control for dualrate sampling systems [J]. Acta Automatica Sinica, 2015, 41(10): 1788-1797.
[22] WEI G, WANG L, LIU Y. H control for a class of multi-agent systems via a stochastic sampled-data method [J]. IET Control Theory & Applications, 2015, 9(14): 2057-2065.
[23] JU Y, TIAN X, LIU H, et al. Fault detection of networked dynamical systems: a survey of trends and techniques [J]. International Journal of Systems Science, 2021, 52(16): 3390-3409.
[24] GUAN Z H, YANG C, HUANG J. Stabilization of networked control systems with short or long random delays: a new multirate method [J]. International Journal of Robust and Nonlinear Control, 2010, 20(16): 1802-1816.
[25] MOARREF M, RODRIGUES L. Stability and stabilization of linear sampled-data systems with multi-rate samplers and time driven zero order holds [J]. Automatica, 2014, 50(10): 2685-2691.
[26] OHSHIMA M, HASHIMOTO I, OHNO H, et al. Multirate multivariable model predictive control and its application to a polymerization reactor [J]. International Journal of Control, 1994, 59(3): 731-742.
[27] GENG H, LIANG Y, YANG F, et al. Model-reduced fault detection for multi-rate sensor fusion with unknown inputs [J]. Information Fusion, 2017, 33: 1-14.
[28] GENG H, LIANG Y, YANG F, et al. The joint optimal filtering and fault detection for multi-rate sensor fusion under unknown inputs [J]. Information Fusion, 2016, 29: 57-67.
[29] IZADI I, ZHAO Q, CHEN T. An H approach to fast rate fault detection for multirate sampled-data systems [J]. Journal of Process Control, 2006, 16(6): 651-658.
[30] ZHANG P, DING S X, WANG G Z, et al. Fault detection for multirate sampled-data systems with time delays [J]. International Journal of Control, 2002, 75(18): 1457-1471.
[31] ZHONG M, YE H, DING S X, et al. Observer-based fast rate fault detection for a class of multirate sampled-data systems [J]. IEEE Transactions on Automatic Control, 2007, 52(3): 520-525.
[32] ZHANG Y, WANG Z, ZOU L, et al. Fault detection filter design for networked multi-rate systems with fading measurements and randomly occurring faults [J]. IET Control Theory & Applications, 2016, 10(5): 573-581.
[33] LIANG Y, CHEN T, PAN Q. Multi-rate optimal state estimation [J]. International Journal of Control, 2009, 82(11): 2059-2076.
[34] YAN L, JIANG L, XIA Y, et al. State estimation and data fusion for multirate sensor networks [J]. International Journal of Adaptive Control and Signal Processing, 2016, 30(1): 3-15.
[35] ZHANG H, BASIN M V, SKLIAR M. It–Volterra optimal state estimation with continuous, multirate, randomly sampled, and delayed measurements [J]. IEEE Transactions on Automatic Control, 2007, 52(3): 401-416.
[36] GENG H, LIANG Y, ZHANG X. Linear-minimum-mean-square-error observer for multi-rate sensor fusion with missing measurements [J]. IET Control Theory & Applications, 2014, 8(14): 1375-1383.
[37] ZOU L, WANG Z, HU J, et al. Communication-protocol-based analysis and synthesis of networked systems: progress, prospects and challenges [J]. International Journal of Systems Science, 2021, 52(14): 3013-3034.
[38] HU J, ZHANG H, LIU H, et al. A survey on sliding mode control for networked control systems [J]. International Journal of Systems Science, 2021, 52(6): 1129-1147.
[39] WEI G, LIU L, WANG L, et al. Event-triggered control for discrete-time systems with unknown nonlinearities: an interval observer-based approach [J]. International Journal of Systems Science, 2020, 51(6): 1019-1031.
[40] ZHANG P, YUAN Y, GUO L. Fault-tolerant optimal control for discrete-time nonlinear system subjected to input saturation: a dynamic event-triggered approach [J]. IEEE Transactions on Cybernetics, 2019, 51(6): 2956-2968.
[41] SUN Y, DING D, DONG H, et al. Event-based resilient filtering for stochastic nonlinear systems via innovation constraints [J]. Information Sciences, 2021, 546: 512-525.
[42] HU J, WANG Z, ALSAADI F E, et al. Event-based filtering for time-varying nonlinear systems subject to multiple missing measurements with uncertain missing probabilities [J]. Information Fusion, 2017, 38: 74-83.
[43] DING D, WANG Z, HAN Q L. A set-membership approach to event-triggered filtering for general nonlinear systems over sensor networks [J]. IEEE Transactions on Automatic Control, 2019, 65(4): 1792-1799.
[44] NOWZARI C, GARCIA E, CORTéS J. Event-triggered communication and control of networked systems for multi-agent consensus [J]. Automatica, 2019, 105: 1-27.
[45] ?ARZéN K E. A simple event-based PID controller [J]. IFAC Proceedings Volumes, 1999, 32(2): 8687-8692.
[46] BOYD S, EL GHAOUI L, FERON E, et al. Linear matrix inequalities in system and control theory[M]. Philadelphia: Society for Industrial and Applied Mathematics, 1994: 55-87.
[47] HAN F, WEI G, DING D, et al. Finite-horizon H-consensus control for multi-agent systems with random parameters: the local condition case [J]. Journal of the Franklin Institute, 2017, 354(14): 6078-6097.
[1] 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.
[2] 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.
[3] 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.
[4] 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.
[5] 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.
[6] 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.
[7] 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.
[8] 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.
[9] 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!