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Stochastic Double Array Analysis and Convergence of Consensus Algorithms with Noisy Measurements

Huang , Minyi; Manton, Jonathan


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.

CollectionsANU Research Publications
Date published: 2007
Type: Conference paper
Source: Proceedings of the 2007 American Control Conference
DOI: 10.1109/ACC.2007.4282534


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