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Consensus seeking via iterative learning for multi‐agent systems with switching topologies and communication time‐delays
Author(s) -
Meng Deyuan,
Jia Yingmin,
Du Junping
Publication year - 2016
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.3534
Subject(s) - iterative learning control , convergence (economics) , network topology , multi agent system , monotonic function , computer science , consensus , trajectory , mathematical optimization , control theory (sociology) , interval (graph theory) , control (management) , mathematics , artificial intelligence , mathematical analysis , physics , combinatorics , astronomy , economics , economic growth , operating system
Summary This paper deals with the high‐precision consensus seeking problem of multi‐agent systems when they are subject to switching topologies and varying communication time‐delays. By combining the iterative learning control (ILC) approach, a distributed consensus seeking algorithm is presented based on only the relative information between every agent and its local (or nearest) neighbors. All agents can be enabled to achieve consensus exactly on a common output trajectory over a finite time interval. Furthermore, conditions are proposed to guarantee both exponential convergence and monotonic convergence for the resulting ILC processes of multi‐agent consensus systems. In particular, the linear matrix inequality technique is employed to formulate the established convergence conditions, which can directly give formulas for the gain matrix design. An illustrative example is included to validate the effectiveness of the proposed ILC‐motivated consensus seeking algorithm. Copyright © 2016 John Wiley & Sons, Ltd.