文献类型：期刊文献

英文题名：Consensus Seeking of Multi-agent Systems from an Iterative Learning Perspective

作者：Li, Juntao[1];Wang, Yadi[1];Xiao, Huimin[2]

第一作者：Li, Juntao

通讯作者：Li, JT[1]

机构：[1]Henan Normal Univ, Sch Math & Informat Sci, Henan Engn Lab Big Data Stat Anal & Optimal Contr, Xinxiang 453007, Peoples R China;[2]Henan Univ Econ & Law, Sch Math & Informat Sci, Zhengzhou 450002, Peoples R China

第一机构：Henan Normal Univ, Sch Math & Informat Sci, Henan Engn Lab Big Data Stat Anal & Optimal Contr, Xinxiang 453007, Peoples R China

通讯机构：[1]corresponding author), Henan Normal Univ, Sch Math & Informat Sci, Henan Engn Lab Big Data Stat Anal & Optimal Contr, Xinxiang 453007, Peoples R China.

年份：2016

卷号：14

期号：5

起止页码：1173-1182

外文期刊名：INTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS

收录：;EI(收录号：20163102663833);Scopus(收录号：2-s2.0-84979658176);WOS:【SCI-EXPANDED(收录号:WOS:000384390900003)】；

基金：This work was supported by National Natural Science Foundation of China (61374079, 61203293, 61473010), Key Scientific and Technological Project of Henan Province (122102210131), Program for Science and Technology Innovation Talents in Universities of Henan Province (13HASTIT040), Henan Higher School Funding Scheme for Young Teachers (2012GGJS-063), Program for Innovative Research Team (in Science and Technology) in University of Henan Province (14IRTSTHN023).

语种：英文

外文关键词：Consensus seeking; directed networks; iterative learning; learning convergence theory; monotonic convergence; multi-agent networks

摘要：The consensus seeking problems for both discrete and continuous multi-agent networks are discussed from an iterative learning perspective. It is shown that the consensus seeking process can be viewed as an iterative learning process for agents under directed networks to improve their performances from time to time in order to achieve consensus. If a desired consensus state is specified, then the multi-agent system can be guaranteed to reach consensus through reducing the tracking error between each agent's state and the desired consensus state monotonically to zero with respect to the increasing of time. If there is no desired consensus state, then the agents can achieve consensus through reducing their states monotonically to the minimum quantity with increasing time. Simulations illustrate the observed results.