符号网络下多智能体系统二分一致性的牵制控制问题

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.

Leader-following bipartite consensus of multi-agent systems under signed networks

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.