Distributed average consensus over random networks

S. Sh Alaviani, N. Elia

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations

Abstract

In this paper, the distributed consensus problem of multi-agent networked systems is considered where agents make decisions using local information in the presence of random communication topologies. This problem is included in the framework given in [31]-[32] that allows random interconnection topologies to have distributions possibly depending on each other or time. It is shown that the random Krasnoselskii-Mann iterative algorithm converges almost surely and in mean square to the average of initial states of the agents under suitable assumptions. The algorithm does not require the distribution of random interconnection topologies or B-connectivity assumption for convergence. Therefore, it applies to asynchronous updates or/and unreliable communication protocols. We also show that the algorithm converges for synchronous updates when the weighted graph matrix is periodic and irreducible. It is shown that the agents interact among themselves to approach the consensus subspace in such a way that the projection of their states onto the consensus subspace at each time is equal to the average of their initial states. Eventually, a numerical example is given to exhibit the results.

Original languageEnglish (US)
Title of host publication2019 American Control Conference, ACC 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1854-1859
Number of pages6
ISBN (Electronic)9781538679265
DOIs
StatePublished - Jul 2019
Event2019 American Control Conference, ACC 2019 - Philadelphia, United States
Duration: Jul 10 2019Jul 12 2019

Publication series

NameProceedings of the American Control Conference
Volume2019-July
ISSN (Print)0743-1619

Conference

Conference2019 American Control Conference, ACC 2019
Country/TerritoryUnited States
CityPhiladelphia
Period7/10/197/12/19

Bibliographical note

Funding Information:
This work was supported by National Science Foundation under Grant CCF-1320643, AFOSR Grant FA 9550-15-1-0119, and Grant ECCS-1509372.

Publisher Copyright:
© 2019 American Automatic Control Council.

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