Open AccessMulti‐agent iterative learning control with communication topologies dynamically changing in two directions
Author(s) -
Meng Deyuan,
Jia Yingmin,
Du Junping
Publication year - 2013
Publication title -
iet control theory and applications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.059
H-Index - 108
eISSN - 1751-8652
pISSN - 1751-8644
DOI - 10.1049/iet-cta.2012.0812
Subject(s) - iterative learning control , convergence (economics) , multi agent system , network topology , consensus , computer science , control theory (sociology) , protocol (science) , construct (python library) , scheme (mathematics) , control (management) , topology (electrical circuits) , distributed computing , mathematics , artificial intelligence , computer network , medicine , mathematical analysis , alternative medicine , pathology , combinatorics , economics , economic growth
This study aims to develop an iterative learning control (ILC) approach to solving finite‐time output consensus problems of multi‐agent systems. The communication topologies among agents are considered to dynamically change in two directions (along both time axis and iteration axis), for which a framework is presented to construct effective distributed protocols. It is shown that a protocol can be derived through ILC to enable multi‐agent systems to accomplish the finite‐time consensus, which moreover can possess an exponentially fast convergence speed. In particular, for any desired terminal output that is available to not all of but only a portion of agents, multi‐agent systems can be guaranteed to achieve the finite‐time consensus at the desired terminal output. Simulation tests are given to demonstrate the performance and effectiveness of the obtained consensus results.