Stochastic Lyapunov analysis for consensus algorithms with noisy measurements

Minyi Huang*, Jonathan H. Manton

*Corresponding author for this work

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    30 Citations (Scopus)


    This paper studies the coordination and consensus of networked agents in an uncertain environment. We consider a group of agents on an undirected graph with fixed topology, but differing from most existing work, each agent has only noisy measurements of its neighbors' states. Traditional consensus algorithms in general cannot deal with such a scenario. For consensus seeking, we introduce stochastic approximation type algorithms with a decreasing step size. We present a stochastic Lyaponuv analysis based upon the total mean potential associated with the agents. Subsequently, the so-called direction of invariance is introduced, which combined with the decay property of the stochastic Lyapunov function leads to mean square convergence of the consensus algorithm.

    Original languageEnglish
    Title of host publicationProceedings of the 2007 American Control Conference, ACC
    Number of pages6
    Publication statusPublished - 2007
    Event2007 American Control Conference, ACC - New York, NY, United States
    Duration: 9 Jul 200713 Jul 2007

    Publication series

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


    Conference2007 American Control Conference, ACC
    Country/TerritoryUnited States
    CityNew York, NY


    Dive into the research topics of 'Stochastic Lyapunov analysis for consensus algorithms with noisy measurements'. Together they form a unique fingerprint.

    Cite this