DocumentCode :
175437
Title :
Stochastic consensus of linear multi-agent systems: Communication noises and Markovian switching topologies
Author :
Long Cheng ; Yunpeng Wang ; Zeng-Guang Hou ; Min Tan
Author_Institution :
State Key Lab. of Manage. an Control for Complex, Inst. of Autom., Beijing, China
fYear :
2014
fDate :
May 31 2014-June 2 2014
Firstpage :
274
Lastpage :
279
Abstract :
This paper studies the mean square and almost sure consensus of discrete-time linear multi-agent systems with communication noises under Markovian switching topologies. By a sophisticated stochastic-approximation type protocol, the closed-loop dynamics of this linear multi-agent system can be transformed into a discrete-time first-order integral multi-agent system. It is proved that if all roots of a polynomial, whose coefficients are the parameters in the gain vector of the proposed protocol, are in the unit circle, there is certain equivalence between the consensus of original linear multi-agent system and the consensus of transformed first-order integral multi-agent system. Then some sufficient conditions on the mean square/almost sure consensus of linear multi-agent systems can be obtained accordingly. Finally, theoretical analysis is verified by simulation examples.
Keywords :
Markov processes; closed loop systems; discrete time systems; linear systems; polynomials; robot dynamics; stochastic systems; vectors; Markovian switching topology; closed-loop dynamics; communication noise; discrete-time first-order integral multiagent system; discrete-time linear multiagent systems; gain vector; mean square; polynomial; stochastic consensus; stochastic-approximation type protocol; unit circle; Multi-agent systems; Noise; Protocols; Random variables; Stochastic processes; Switches; Topology; Consensus; Linear time-invariant dynamics; Markovian switching topology; Multi-agent systems; Stochastic noise;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Control and Decision Conference (2014 CCDC), The 26th Chinese
Conference_Location :
Changsha
Print_ISBN :
978-1-4799-3707-3
Type :
conf
DOI :
10.1109/CCDC.2014.6852158
Filename :
6852158
Link To Document :
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