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符号网络下多智能体系统二分一致性的牵制控制问题
引用本文:邵海滨,潘鹿鹿,席裕庚,李德伟,甘中学,许裕栗.符号网络下多智能体系统二分一致性的牵制控制问题[J].控制与决策,2019,34(8):1695-1701.
作者姓名:邵海滨  潘鹿鹿  席裕庚  李德伟  甘中学  许裕栗
作者单位:上海交通大学自动化系,上海200240,上海交通大学自动化系,上海200240,上海交通大学自动化系,上海200240,上海交通大学自动化系,上海200240,上海泛智能源装备有限公司,上海201400,上海泛智能源装备有限公司,上海201400
基金项目:国家自然科学基金项目(61433002, 61521063, 61333009, 61473317, 61590924);国家重点基础研究发展计划项目(2014CB249200).
摘    要:一致性是多智能体系统分布式协同控制的核心.以往关于一致性问题的研究大多集中在个体间只有正权重相互作用的网络中.现研究符号网络(网络中个体间既存在正权重相互作用又存在负权重相互作用)下二分一致性的牵制控制问题.针对外界输入仅作用于网络节点二元划分的同一簇个体和外界输入分别作用于网络节点二元划分的两簇中个体两种情形,给出其二分一致性稳态值的定量化描述,即如果外界输入只控制其中一簇的个体,则当外界输入作用为正(负)权重时,受到外界输入直接影响的 一簇个体的状态收敛到外界输入(外界输入的相反数),另一簇个体状态收敛到外界输入的相反数(外界输入);如果外界输入以相反的权重符号分别控制两簇中的个体,则由正(负)权重外界输入控制的一簇个体状态收敛到外界输入(外界输入的相反数),另一簇个体状态收敛到外界输入的相反数(外界输入).仿真研究验证了所提出理论的有效性.

关 键 词:符号网络  二分一致性  牵制控制  多智能体系统  吸引-排斥  符号拉普拉斯矩阵

Leader-following bipartite consensus of multi-agent systems under signed networks
SHAO Hai-bin,PAN Lu-lu,XI Yu-geng,LI De-wei,GAN Zhong-xue and XU Yu-li.Leader-following bipartite consensus of multi-agent systems under signed networks[J].Control and Decision,2019,34(8):1695-1701.
Authors:SHAO Hai-bin  PAN Lu-lu  XI Yu-geng  LI De-wei  GAN Zhong-xue and XU Yu-li
Institution:Department of Automation,Shanghai Jiao Tong University,Shanghai 200240,China,Department of Automation,Shanghai Jiao Tong University,Shanghai 200240,China,Department of Automation,Shanghai Jiao Tong University,Shanghai 200240,China,Department of Automation,Shanghai Jiao Tong University,Shanghai 200240,China,Shanghai Fan-Zhi Energy Equipment Co.Ltd,Shanghai201400,China and Shanghai Fan-Zhi Energy Equipment Co.Ltd,Shanghai201400,China
Abstract:Consensus protocols play a central role in the coordination distributed of multi-agent systems. Previous studies are mostly concentrated on the networks with only non-negative weighted edges. This paper examines the leader-following bipartite consensus problem under signed networks, where both positively and negatively weighted edges are allowed. A quantitative characterization of bipartite consensus is given according to the influence pattern of external inputs. If the agents in only one of the two clusters are influenced by external inputs, then agents in the cluster that is directly influenced by positively (resp. negatively) weighted external inputs will reach a consensus on external inputs (resp. opposite number of the external inputs), and agents in the other cluster reach a consensus on the opposite number of the external inputs (resp. external inputs). On the other hand, if agents in both clusters are influenced by external inputs with opposite signs of weights, then agents in the cluster that is directly influenced by positively (resp. negatively) weighted external input reach a consensus on external inputs (resp. opposite number of the external inputs), and agents in the other cluster will reach a consensus on the opposite number of the external inputs (resp. external inputs). Simulation results show the effectiveness of the theoretical results.
Keywords:
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