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Formation control for multi‐agent systems through an iterative learning design approach
Author(s) -
Meng Deyuan,
Jia Yingmin
Publication year - 2012
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.2890
Subject(s) - iterative learning control , convergence (economics) , multi agent system , monotonic function , computer science , control theory (sociology) , stability (learning theory) , exponential stability , mathematical optimization , interval (graph theory) , control (management) , linear matrix inequality , mathematics , artificial intelligence , nonlinear system , machine learning , mathematical analysis , physics , quantum mechanics , combinatorics , economics , economic growth
SUMMARY This paper deals with formation control problems for multi‐agent systems by using iterative learning control (ILC) design approaches. Distributed formation ILC algorithms are presented to enable all agents in directed graphs to achieve the desired relative formations perfectly over a finite‐time interval. It is shown that not only asymptotic stability but also monotonic convergence of multi‐agent formation ILC can be accomplished, and the convergence conditions in terms of linear matrix inequalities can be simultaneously established. The derived results are also applicable to multi‐agent systems that are subject to stochastic disturbances and model uncertainties. Furthermore, the feasibility of convergence conditions and the effect of communication delays are discussed for the proposed multi‐agent formation ILC algorithms. Simulation results are given for uncertain multi‐agent systems to verify the theoretical study. Copyright © 2012 John Wiley & Sons, Ltd.