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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...
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...
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