Fast Flocking Algorithm for Multi-agent Systems by Optimizing Local Interactive Topology
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摘要: 针对多智能体系统在动态演化过程中容易出现的"局部聚集"现象,融 合复杂网络中的拓扑结构优化理论与多智能体系统协调蜂拥控制研究,提出了一种基 于邻域交互结构优化的多智能体快速蜂拥控制算法.该算法首先从宏观上分析多智 能体的局部聚集现象,利用社团划分算法将局部相对密集的多个智能体聚类成一个 社团,整个多智能体系统可以划分成多个相对稀疏的社团,并为每个社团选择度 最大的个体作为信息智能体,该个体可以获知虚拟领导者信息;随后从多智能体 系统中不同社团相邻个体间的局部交互结构入手,取消社团间相邻个体的交 互作用,设计仅依赖于社团内部邻居个体交互作用的蜂拥控制律;理论分 析表明,只要每个社团存在一个信息智能体,在虚拟领导者的引导作用下,整个多 智能体系统就可以实现收敛的蜂拥控制行为;仿真实验也证实了对多智 能体系统进行邻域交互结构优化可以有效提高整个系统的收敛速度.Abstract: A fast flocking algorithm for multi-agent systems is presented to improve the speed of consensus of multi-agent systems based on local interactive topology optimization by considering the phenomenon of "local cohesion" in the dynamic process of flocking control, and combining the theory of topology optimization with the research of multi-agent flocking control. Firstly, the phenomenon of "local cohesion" is analyzed macroscopically, and fast Newman algorithm is used to form multiple communities for multi-agent system where agents connect familiarly in the same community and connect sparsely in the different community. Then the agent which possesses the maximum degree is defined as the informed agent in every community which can obtain information from the virtual leader. Furthermore, to cut off the joint among agents in different communities, the flocking control law is proposed to only consider local interactions between neighbor agents in the same community. Theoretic analysis shows that the multi-agent system can achieve the goal of flocking control when every community has at least one informed agent. And the simulation results show that the speed of flocking control can be improved by optimizing the local interactive topology.
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Key words:
- Multi-agent system /
- flocking control /
- fast consensus /
- local interactive topology
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[1] Reynolds C W. Flocks, herds and schools: a distributed behavioral model. Computer Graphics, 1987, 21(4): 25-34 [2] Luo Xiao-Yuan, Yang Fan, Li Shao-Bao, Guan Xin-Ping. Generation of optimally persistent formation for multi-agent systems. Acta Automatica Sinica, 2014, 40(7): 1311-1319(罗小元, 杨帆, 李绍宝, 关新平. 多智能体系统的最优持久编队生成策略. 自动化学报, 2014, 40(7): 1311-1319) [3] You K Y, Li Z K, Xie L H. Consensus condition for linear multi-agent systems over randomly switching topologies. Automatica, 2013, 49(10): 3125-3132 [4] Su H S, Chen M Z Q, Lam J, Lin Z L. Semi-global leader-following consensus of linear multi-agent systems with input saturation via low gain feedback. IEEE Transactions on Circuits and Systems I: Regular Papers, 2013, 60(7): 1881-1889 [5] Liu Z W, Guan Z H, Shen X M, Feng G. Consensus of multi-agent networks with aperiodic sampled communication via impulsive algorithms using position-only measurements. IEEE Transactions on Automatic Control, 2012, 57(10): 2639-2643 [6] Olfati-Saber R. Flocking for Multi-Agent Dynamic Systems: Algorithms and Theory. IEEE Transactions on Automatic Control, 2006, 51(3): 401-420 [7] Su H S, Wang X F, Lin Z L. Flocking of multi-agents with a virtual leader. IEEE Transactions on Automatic Control, 2009, 54(2): 293-307 [8] Ge Y R, Chen Y Z, Zhang Y X. Average dwell-time conditions for consensus of discrete-time linear multi-agent systems with switching topologies and time-varying delays. Acta Automatica Sinica, 2014, 40(11): 2609-2617 [9] Yan H C, Shen Y C, Zhang H, Shi H B. Decentralized event-triggered consensus control for second-order multi-agent systems. Neurocomputing, 2014, 133: 18-24 [10] Wang X F, Li X, Lu J H. Control and flocking of networked systems via pinning. IEEE Circuits and Systems Magazine, 2010, 10(3): 83-91 [11] Martin S, Girard A, Fazeli A, Jadbabaie A. Multiagent flocking under general communication rule. IEEE Transactions on Control of Network Systems, 2014, 1(2): 155-166 [12] Martin S. Multi-agent flocking under topological interactions. Systems and Control Letters, 2014, 69: 53-61 [13] Newman M E J. Fast algorithm for detecting community structure in networks. Physical Review E, 2004, 69(6): 066133 [14] Chen M M, Kuzmin K, Szymanski B K. Community detection via maximization of modularity and its variants. IEEE Transactions on Computational Social Systems, 2014, 1(1): 46-65 [15] Chen Q, Wu T T, Fang M. Detecting local community structures in complex networks based on local degree central nodes. Physica A: Statistical Mechanics and its Applications, 2013, 392(3): 529-537
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