引用本文:魏永东,李宗刚,杜亚江,陈引娟.测量受限线性多智能体系统一致性迭代学习控制[J].控制理论与应用,2021,38(7):963~970.[点击复制]
WEI Yong-dong,LI Zong-gang,DU Ya-jiang,CHEN Ying-juan.Iterative learning control for consensus of measurement-constrained linear multi-agent systems[J].Control Theory and Technology,2021,38(7):963~970.[点击复制]
测量受限线性多智能体系统一致性迭代学习控制
Iterative learning control for consensus of measurement-constrained linear multi-agent systems
摘要点击 2062  全文点击 890  投稿时间:2020-10-27  修订日期:2021-06-14
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DOI编号  10.7641/CTA.2021.00751
  2021,38(7):963-970
中文关键词  多智能体系统  输出一致性  测量受限  迭代学习控制  分布式协议  切换拓扑
英文关键词  multi-agent system  output consensus  measurement restricted  iterative learning control  distributed protocol  switching topology
基金项目  国家自然科学基金项目(61663020), 甘肃省高等学校科研项目成果转化项目(2018D–10), 兰州交通大学“百人计划”项目资助.
作者单位E-mail
魏永东 兰州交通大学 948316853@qq.com 
李宗刚* 兰州交通大学 lizongg@126.com 
杜亚江 兰州交通大学  
陈引娟 兰州交通大学  
中文摘要
      本文针对通讯拓扑同时沿时间轴和迭代轴切换且存在测量受限的情形, 研究了基于迭代学习控制方法的 连续线性多智能体系统输出一致性跟踪问题. 在系统通信拓扑始终含有以虚拟领航者为根节点的生成树, 以及所有 智能体初态在每次迭代均可重置的条件下, 针对跟随者能够获得的局部信息而设计了测量受限分布式输出一致性 协议. 然后, 利用λ范数的方法和圆盘定理给出了所有跟随者的输出收敛到虚拟领导者输出的两个充分性条件, 其 中之一可实现时变迭代学习增益的分布式计算. 最后, 仿真结果验证了所得结论的有效性.
英文摘要
      In this paper, aiming at the situation where the communication xtopology is switched along the time axis and iterative axis, and with measurement restricted, the output consensus tracking problem of the continuous linear multi-agent system based on the iterative learning method is studied. Under the condition that the system communication topology always contains a spanning tree with the virtual leader as the root node, and the initial state of all agents can be reset in each iteration, a measurement-constrained distributed output consensus protocol designed for the local information that followers can obtain. And then, using λ norm method and the disc theorem give two sufficient conditions for the output of all followers to converge to the virtual leader’s output, one of which can realize distributed real-time calculation of time-varying iterative learning gain. Finally, the corresponding simulation results verify the strategy effectiveness.