Stochastic Double Array Analysis and Convergence of Consensus Algorithms with Noisy Measurements
Date
2007
Authors
Huang , Minyi
Manton, Jonathan
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Publisher
Institute of Electrical and Electronics Engineers (IEEE Inc)
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.
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Keywords
Keywords: Agents; Modal analysis; Stabilizers (agents); Stochastic models; Stochastic programming; (R ,S)-symmetric; agent modeling; consensus algorithms; Convergence (mathematics); Double arrays; Noisy measurements; step size; Stochastic approximation (SA); Uncert
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Source
Proceedings of the 2007 American Control Conference
Type
Conference paper
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2037-12-31
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