多率采样机制下多智能体动态事件触发二分一致性研究

doi:10.19678/j.issn.1000-3428.0067344

摘要/Abstract

摘要：

针对满足细节平衡结构的对抗交互多智能体系统，研究多率采样机制下其动态事件触发二分一致性问题。在多率采样机制下，通过构造一个多率缓存器解决多率采样时序不匹配的问题，设计一类多率观测器便于获得系统的估计状态以实现目标控制。通过引入动态事件触发机制，多率观测器在事件触发时刻广播其状态数据至通信网络中的邻居智能体。每个智能体基于触发时刻的观测器状态信息，利用一组开环估计器以获得连续的智能体状态估计值。在此基础上，设计一个分布式控制协议，利用代数黎卡提方程和李雅普诺夫稳定性理论证明系统可以在细节平衡的多智能体系统通信网络中实现二分一致性，并且排除动态事件触发可能导致的芝诺行为。通过一个包含3种不同采样机制和控制方案的对比仿真，证明了所提控制方案的有效性，表明多率采样机制相较于传统的单率采样机制具有更快和更稳定的收敛性能，并且动态事件触发相较于静态事件触发可以进一步降低触发次数。

关键词: 多智能体系统, 多率采样机制, 二分一致性, 动态事件触发机制, 细节平衡结构

Abstract:

Aiming at a Multi-Agent System(MAS) with a detail-balanced antagonistic interaction structure, this study investigates the dynamic event-triggered bipartite consensus problem with a multi-rate sampling mechanism. First, the timing mismatch problem of the multi-rate sampling mechanism is solved by constructing a multi-rate buffer. Moreover, a class of multi-rate observers is designed to obtain the estimated state of the system to achieve the control objective. Second, by introducing a dynamic event-triggered mechanism, the multi-rate observer broadcasts its state data to the neighboring agents in the communication network at event-triggered instants. Each agent uses a set of open-loop estimators to obtain continuous agent state estimates based on the observer states at trigger instants. Next, a distributed control protocol is designed. Using the algebraic Riccati equation and the Lyapunov stability theory, it has been proven that MASs can achieve a bipartite consensus in a communication network with a detail-balanced structure. Furthermore, the Zeno behavior that the dynamic event-triggered mechanism may have caused is excluded. Finally, a simulation comparison, including three different sampling mechanisms and control schemes, is provided to illustrate the effectiveness of the proposed control scheme. It is shown that the multi-rate sampling mechanism has a faster and more stable convergence performance than the traditional single-rate sampling mechanism, and the dynamic event-triggered mechanism can further reduce the number of triggers compared with the static event-triggered mechanism.

Key words: Multi-Agent System(MAS), multi-rate sampling mechanism, bipartite consensus, dynamic event-triggered mechanism, detail-balanced structure

范晓宇, 贾新春, 李彬, 谢云飞. 多率采样机制下多智能体动态事件触发二分一致性研究[J]. 计算机工程, 2024, 50(3): 114-121.

Xiaoyu FAN, Xinchun JIA, Bin LI, Yunfei XIE. Research on Dynamic Event-Triggered Bipartite Consensus of Multi-Agent System with a Multi-Rate Sampling Mechanism[J]. Computer Engineering, 2024, 50(3): 114-121.

https://www.ecice06.com/CN/Y2024/V50/I3/114

图/表 8

图1 多率采样机制下动态事件触发二分一致性控制框架

Fig.1 The framework of dynamic event-triggered bipartite consensus control with a multi-rate sampling mechanism

图2 带有细节平衡结构的网络拓扑

Fig.2 A network topology with a detail-balanced structure

图3 多率采样动态事件触发控制下9个智能体的状态轨迹

Fig.3 The states trajectories of nine agents under a dynamic event-triggered control with a multi-rate sampling mechanism

图4 多率采样机制下各智能体动态事件触发时刻

Fig.4 The dynamic event-triggered instants of each agent with a multi-rate sampling mechanism

图5 多率采样机制下动态事件触发辅助参数状态轨迹

Fig.5 The state trajectories of dynamic event-triggered auxiliary parameters with a multi-rate sampling mechanism

图6 3种控制方案下收敛性能对比

Fig.6 Comparison of convergence performance under three control schemes

参考文献 26

1	HANSEL D, SOMPOLINSKY H. Synchronization and computation in a chaotic neural network. Physical Review Letters, 1992, 68 (5): 718- 721. doi: 10.1103/PhysRevLett.68.718
2	NING B D, HAN Q L, LU Q A. Fixed-time leader-following consensus for multiple wheeled mobile robots. IEEE Transactions on Cybernetics, 2020, 50 (10): 4381- 4392. doi: 10.1109/TCYB.2019.2955543
3	YANG K, LI C H, JING X, et al. Energy dispatch optimization of islanded multi-microgrids based on symbiotic organisms search and improved multi-agent consensus algorithm. Energy, 2022, 239, 122105. doi: 10.1016/j.energy.2021.122105
4	GE X H, HAN Q L, WANG Z D. A threshold-parameter-dependent approach to designing distributed event-triggered H_∞ consensus filters over sensor networks. IEEE Transactions on Cybernetics, 2019, 49 (4): 1148- 1159. doi: 10.1109/TCYB.2017.2789296
5	OLFATI-SABER R, MURRAY R M. Consensus problems in networks of agents with switching topology and time-delays. IEEE Transactions on Automatic Control, 2004, 49 (9): 1520- 1533. doi: 10.1109/TAC.2004.834113
6	AMIRKHANI A, BARSHOOI A H. Consensus in multi-agent systems: a review. Artificial Intelligence Review, 2022, 55 (5): 3897- 3935. doi: 10.1007/s10462-021-10097-x
7	ALTAFINI C. Consensus problems on networks with antagonistic interactions. IEEE Transactions on Automatic Control, 2013, 58 (4): 935- 946. doi: 10.1109/TAC.2012.2224251
8	LIU M A, WANG X K, LI Z K. Robust bipartite consensus and tracking control of high-order multiagent systems with matching uncertainties and antagonistic interactions. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2020, 50 (7): 2541- 2550. doi: 10.1109/TSMC.2018.2821181
9	CAI Y L, ZHANG H G, WANG Y C, et al. Adaptive bipartite fixed-time time-varying output formation-containment tracking of heterogeneous linear multiagent systems. IEEE Transactions on Neural Networks and Learning Systems, 2022, 33 (9): 4688- 4698. doi: 10.1109/TNNLS.2021.3059763
10	CHEN X, YU H, HAO F. Prescribed-time event-triggered bipartite consensus of multiagent systems. IEEE Transactions on Cybernetics, 2022, 52 (4): 2589- 2598. doi: 10.1109/TCYB.2020.3004572
11	ZOU Y, MENG Z Y, XIA K W. Consensus of cooperative-antagonistic multi-agent networks with asynchronous three-option decision mechanism. Automatica, 2022, 140, 110258. doi: 10.1016/j.automatica.2022.110258
12	CHEN L L, SHI L, ZHOU Q A, et al. Secure bipartite tracking control for linear leader-following multiagent systems under denial-of-service attacks. CAA Journal of Automatica Sinica, 2022, 9 (8): 1512- 1515. doi: 10.1109/JAS.2022.105758
13	CHU T G, WANG L, CHEN T W, et al. Complex emergent dynamics of anisotropic swarms: convergence vs oscillation. Chaos, Solitons & Fractals, 2006, 30 (4): 875- 885.
14	YANG S F, CAO J D, LU J Q. A new protocol for finite-time consensus of detail-balanced multi-agent networks. Chaos: An Interdisciplinary Journal of Nonlinear Science, 2012, 22 (4): 04313.
15	LU J Q, WANG Y Q, SHI X C, et al. Finite-time bipartite consensus for multiagent systems under detail-balanced antagonistic interactions. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2021, 51 (6): 3867- 3875. doi: 10.1109/TSMC.2019.2938419
16	JIA X C, MA W W, YANG F W, et al. Matching mechanism for networked control systems with multirate sampling. IET Control Theory & Applications, 2017, 11 (16): 2848- 2857.
17	ZHANG X M, HAN Q L. A decentralized event-triggered dissipative control scheme for systems with multiple sensors to sample the system outputs. IEEE Transactions on Cybernetics, 2016, 46 (12): 2745- 2757. doi: 10.1109/TCYB.2015.2487420
18	JIA X C, GAO S S, YOU X, et al. Output consensus of heterogeneous multi-agent systems with a multi-sensor multi-rate sampling mechanism. Journal of the Franklin Institute, 2020, 357 (17): 12640- 12669. doi: 10.1016/j.jfranklin.2020.08.022
19	王悦, 贾新春, 游秀, 等. DoS攻击下基于多率采样的多智能体系统安全一致性. 控制理论与应用, 2022, 39 (10): 1890- 1897. doi: 10.7641/CTA.2022.10985
	WANG Y, JIA X C, YOU X, et al. Security consistency of multi-agent system based on multi-rate sampling under DoS attack. Control Theory & Applications, 2022, 39 (10): 1890- 1897. doi: 10.7641/CTA.2022.10985
20	DING L, HAN Q L, GE X H, et al. An overview of recent advances in event-triggered consensus of multiagent systems. IEEE Transactions on Cybernetics, 2018, 48 (4): 1110- 1123. doi: 10.1109/TCYB.2017.2771560
21	XU Y, FANG M, SHI P, et al. Event-based secure consensus of mutiagent systems against DoS attacks. IEEE Transactions on Cybernetics, 2020, 50 (8): 3468- 3476. doi: 10.1109/TCYB.2019.2918402
22	HU A H, WANG Y Y, CAO J D, et al. Event-triggered bipartite consensus of multi-agent systems with switching partial couplings and topologies. Information Sciences, 2020, 521, 1- 13. doi: 10.1016/j.ins.2020.02.038
23	GIRARD A. Dynamic triggering mechanisms for event-triggered control. IEEE Transactions on Automatic Control, 2015, 60 (7): 1992- 1997. doi: 10.1109/TAC.2014.2366855
24	ZHAO D, WANG Z D, WEI G L, et al. A dynamic event-triggered approach to observer-based PID security control subject to deception attacks. Automatica, 2020, 120, 109128. doi: 10.1016/j.automatica.2020.109128
25	YI X L, LIU K, DIMAROGONAS D V, et al. Dynamic event-triggered and self-triggered control for multi-agent systems. IEEE Transactions on Automatic Control, 2019, 64 (8): 3300- 3307. doi: 10.1109/TAC.2018.2874703
26	XU C J, XU H C, GUAN Z H, et al. Observer-based dynamic event-triggered semiglobal bipartite consensus of linear multi-agent systems with input saturation. IEEE Transactions on Cybernetics, 2023, 53 (5): 3139- 3152. doi: 10.1109/TCYB.2022.3164048

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