| 基于社区划分的多智能体网络快速蜂拥控制 |
| |
| 引用本文： | 陈世明,邱昀,刘俊恺,聂森.基于社区划分的多智能体网络快速蜂拥控制[J].控制与决策,2018,33(8):1523-1526. |
| |
| 作者姓名： | 陈世明 邱昀 刘俊恺 聂森 |
| |
| 作者单位： | 华东交通大学电气与自动化工程学院,南昌330013,华东交通大学电气与自动化工程学院,南昌330013,华东交通大学电气与自动化工程学院,南昌330013,华东交通大学电气与自动化工程学院,南昌330013 |
| |
| 基金项目： | 国家自然科学基金项目(11662002);江西省创新驱动“5511”优势科技创新团队项目(20165BCB19011);江西省自然科学基金项目(20171BAB202029);江西省重点研发计划项目(20161BBE53008). |
| |
| 摘 要： | 针对复杂网络社区特性对多智能体系统协同控制效率的影响,面向具有ER(Erdos-renyi)网络或BA (Barabasi-albert)网络性质的多智能体系统,提出一种基于社区划分的快速蜂拥控制算法.该算法充分考虑社区内个体的相对密集特性,通过在社区间引入虚拟领导者作用,避免系统在演化过程中因通信受限而导致的“分块”现象,可有效提高系统拓扑的代数连通度.仿真结果表明:具有相应性质的多智能体系统蜂拥行为的收敛速度与ER和BA网络的平均度以及BA网络度分布的幂指数正相关;优化社区个数有利于提高蜂拥收敛速度.
|
| 关 键 词： | 复杂网络 快速蜂拥控制 社区划分 收敛特性 |
Fast flocking algorithm of multi-agent network via community division |
| |
| CHEN Shi-ming,QIU Yun,LIU Jun-kai and NIE Sen.Fast flocking algorithm of multi-agent network via community division[J].Control and Decision,2018,33(8):1523-1526. |
| |
| Authors: | CHEN Shi-ming QIU Yun LIU Jun-kai and NIE Sen |
| |
| Affiliation: | School of Electrical and Automation Engineering,East China Jiaotong University,Nanchang 330013,China,School of Electrical and Automation Engineering,East China Jiaotong University,Nanchang 330013,China,School of Electrical and Automation Engineering,East China Jiaotong University,Nanchang 330013,China and School of Electrical and Automation Engineering,East China Jiaotong University,Nanchang 330013,China |
| |
| Abstract: | Aiming at the effect of community characteristics of complex networks on collaborative control of multi-agent systems,a fast flocking control algorithm based on community division is proposed for the multi-agent system with the characteristic of the Erdos-renyi(ER) random network and Barabasi-albert(BA) scale-free network, which considers the relative concentration characteristics of agents in every community and incorporate attractive force of virtual leader between different communities, so the "block" phenomenon in evolution process because of communication constraint can be avoided and algebraic connectivity of the system is improved. Simulation results show that the convergence characteristics of the flocking behavior of the multi-agent system with complex networks are affected by the average degree of ER and BA networks and the power exponent of the degree of BA network distribution, and optimizing the number of partition communities is beneficial to improve the convergence speed. |
| |
| Keywords: | |
|
| 点击此处可从《控制与决策》浏览原始摘要信息 |
|
点击此处可从《控制与决策》下载全文 |
|
