Stochastic double array analysis and convergence of consensus algorithms with noisy measurements

Minyi Huang; Jonathan H. Manton

doi:10.1109/ACC.2007.4282534

Stochastic double array analysis and convergence of consensus algorithms with noisy measurements

Minyi Huang^*, Jonathan H. Manton

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

21 Citations (Scopus)

Abstract

This paper considers consensus-seeking of networked agents in an uncertain environment where each agent has noisy measurements of its neighbors' states. We propose stochastic approximation type algorithms with a decreasing step size. We first establish consensus results in a two-agent model via a stochastic double array analysis. Next, we generalize the analysis to a class of well studied symmetric models and obtain consensus results.

Original language	English
Title of host publication	Proceedings of the 2007 American Control Conference, ACC
Pages	705-710
Number of pages	6
DOIs	https://doi.org/10.1109/ACC.2007.4282534
Publication status	Published - 2007
Event	2007 American Control Conference, ACC - New York, NY, United States Duration: 9 Jul 2007 → 13 Jul 2007

Publication series

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

Conference

Conference	2007 American Control Conference, ACC
Country/Territory	United States
City	New York, NY
Period	9/07/07 → 13/07/07

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10.1109/ACC.2007.4282534

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title = "Stochastic double array analysis and convergence of consensus algorithms with noisy measurements",

abstract = "This paper considers consensus-seeking of networked agents in an uncertain environment where each agent has noisy measurements of its neighbors' states. We propose stochastic approximation type algorithms with a decreasing step size. We first establish consensus results in a two-agent model via a stochastic double array analysis. Next, we generalize the analysis to a class of well studied symmetric models and obtain consensus results.",

author = "Minyi Huang and Manton, {Jonathan H.}",

year = "2007",

doi = "10.1109/ACC.2007.4282534",

language = "English",

isbn = "1424409888",

series = "Proceedings of the American Control Conference",

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booktitle = "Proceedings of the 2007 American Control Conference, ACC",

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Huang, M & Manton, JH 2007, Stochastic double array analysis and convergence of consensus algorithms with noisy measurements. in Proceedings of the 2007 American Control Conference, ACC., 4282534, Proceedings of the American Control Conference, pp. 705-710, 2007 American Control Conference, ACC, New York, NY, United States, 9/07/07. https://doi.org/10.1109/ACC.2007.4282534

Stochastic double array analysis and convergence of consensus algorithms with noisy measurements. / Huang, Minyi; Manton, Jonathan H.
Proceedings of the 2007 American Control Conference, ACC. 2007. p. 705-710 4282534 (Proceedings of the American Control Conference).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Manton, Jonathan H.

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AB - This paper considers consensus-seeking of networked agents in an uncertain environment where each agent has noisy measurements of its neighbors' states. We propose stochastic approximation type algorithms with a decreasing step size. We first establish consensus results in a two-agent model via a stochastic double array analysis. Next, we generalize the analysis to a class of well studied symmetric models and obtain consensus results.

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M3 - Conference contribution

SN - 1424409888

SN - 9781424409884

T3 - Proceedings of the American Control Conference

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BT - Proceedings of the 2007 American Control Conference, ACC

T2 - 2007 American Control Conference, ACC

Y2 - 9 July 2007 through 13 July 2007

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Stochastic double array analysis and convergence of consensus algorithms with noisy measurements

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