Stochastic Double Array Analysis and Convergence of Consensus Algorithms with Noisy Measurements
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.
|Collections||ANU Research Publications|
|Source:||Proceedings of the 2007 American Control Conference|
|01_Huang _Stochastic_Double_Array_2007.pdf||283.74 kB||Adobe PDF||Request a copy|
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