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Adaptive iterative learning control for coordination of second‐order multi‐agent systems
Author(s) -
Li Jinsha,
Li Junmin
Publication year - 2014
Publication title -
international journal of robust and nonlinear control
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.361
H-Index - 106
eISSN - 1099-1239
pISSN - 1049-8923
DOI - 10.1002/rnc.3055
Subject(s) - iterative learning control , multi agent system , computer science , control theory (sociology) , consensus , adaptive control , lyapunov stability , stability (learning theory) , acceleration , lyapunov function , adaptive learning , control (management) , order (exchange) , mathematical optimization , mathematics , artificial intelligence , nonlinear system , machine learning , physics , finance , classical mechanics , quantum mechanics , economics
SUMMARY In this paper, an efficient framework is proposed to the consensus and formation control of distributed multi‐agent systems with second‐order dynamics and unknown time‐varying parameters, by means of an adaptive iterative learning control approach. Under the assumption that the acceleration of the leader is unknown to any follower agents, a new adaptive auxiliary control and the distributed adaptive iterative learning protocols are designed. Then, all follower agents track the leader uniformly on [0, T ] for consensus problem and keep the desired distance from the leader and achieve velocity consensus uniformly on [0, T ] for the formation problem, respectively. The distributed multi‐agent coordinations performance is analyzed based on the Lyapunov stability theory. Finally, simulation examples are given to illustrate the effectiveness of the proposed protocols in this paper.Copyright © 2013 John Wiley & Sons, Ltd.