摘要
针对多智能体系统蜂拥控制过程中存在效率不高的现象,融合图论和控制理论的分析方法,提出一种基于网络特征重数的牵制蜂拥控制算法.该算法通过网络拓扑结构的邻接矩阵,根据其特征值的最大重数,得出网络可控时所需牵制节点的数目,随后进一步利用控制理论中的PBH判据确定具体的牵制节点,并为之设计牵制蜂拥控制律.与随机牵制策略和度最大牵制策略进行的仿真实验统计结果对比证实了所提算法在提升整个系统控制效率方面的有效性.
An effective pinning strategy was proposed to improve the control efficiency of multi-agent systems based on characteristics multiplicity of network of flocking control,and combining the theory of graph and analytical of control theory method.The algorithm was based on adjacency matrix of the multi-agent network topology structure and according to maximum multiplicity of its eigenvalue,the number of pinned nodes was obtained when the network controllable,and then by using the control theory of PBH（Popov-Belevitch-Hautus）criterion to identify the specific pinned nodes,the pinning flocking control law was designed.With randomly pinning strategy and the maximum degree pinning strategy as a comparative study,the simulation results demonstrate that the proposed algorithm is effective in improving the control efficiency of the entire system.
出处
《华中科技大学学报（自然科学版）》
EI
CAS
CSCD
北大核心
2016年第5期19-24,共6页
Journal of Huazhong University of Science and Technology(Natural Science Edition)
基金
国家自然科学基金资助项目(61364017)
关键词
多智能体
蜂拥控制
特征重数
PBH判据
牵制策略
multi-agent
flocking control
characteristics multiplicity
PBH criterion
pinning strategy
