Consensus Seeking of Multi-agent Systems from an Iterative Learning Perspective

被引：9
作者：
Li, Juntao [1 ]
Wang, Yadi [1 ]
Xiao, Huimin [2 ]
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
来源：
INTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS | 2016年 / 14卷 / 05期
基金：
中国国家自然科学基金;
关键词：
Consensus seeking; directed networks; iterative learning; learning convergence theory; monotonic convergence; multi-agent networks; FINITE-TIME CONSENSUS; DYNAMIC AGENTS; PROTOCOLS; TOPOLOGIES; NETWORKS; TRACKING; DESIGN; DELAYS;
D O I：
10.1007/s12555-015-0103-2
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
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.
引用
收藏
页码：1173 / 1182
页数：10
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