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  • p:\tex\sicon\48-1\67359\67359. dvi - Carleton University
    Abstract This paper considers the coordination and consensus of networked agents where each agent has noisy measurements of its neighbors’ states For consensus seeking, we propose stochastic approximation-type algorithms with a decreasing step size, and introduce the notions of mean square and strong consensus Although the decreasing step size reduces the detrimental effect of the noise
  • Stochastic Consensus Seeking With Noisy and Directed Inter-Agent . . .
    We consider consensus seeking of networked agents on directed graphs where each agent has only noisy measurements of its neighbors' states Stochastic approximation type algorithms are employed so that the individual states converge both in mean square and almost surely to the same limit We further generalize the algorithm to networks with random link failures and prove convergence results
  • Stochastic consensus over noisy networks with Markovian and arbitrary . . .
    In Huang and Manton (2008, 2009, 2010), consensus problems were considered when agents obtain noisy measurements of the states of neighbors, and a stochastic approximation approach was applied to obtain mean square and almost sure convergence in models with fixed network topologies or with independent communications failures (Huang Manton, 2008)
  • Coordination and Consensus of Networked Agents with Noisy Measurements . . .
    Minyi Huang, Jonathan H Manton Coordination and Consensus of Networked Agents with Noisy Measurements: Stochastic Algorithms and Asymptotic Behavior SIAM J Control and Optimization, 48 (1):134-161, 2009 [doi]
  • Coordination and Consensus of Networked Agents with Noisy Measurements . . .
    Summary: This paper considers the coordination and consensus of networked agents where each agent has noisy measurements of its neighbors' states, and proposes stochastic approximation-type algorithms with a decreasing step size, and introduces the notions of mean square and strong consensus
  • Stochastic Consensus Seeking with Measurement Noise: Convergence and . . .
    Minyi Huang and Abstract— We consider consensus seeking with measurement noise in directed graphs containing a spanning tree By using stochastic approximation type algorithms, we show the state of each agent converges in mean square and almost surely to the same limit
  • Stochastic consensus over noisy networks with Markovian and arbitrary . . .
    In Huang and Manton, 2008, Huang and Manton, 2009, Huang and Manton, 2010, consensus problems were considered when agents obtain noisy measurements of the states of neighbors, and a stochastic approximation approach was applied to obtain mean square and almost sure convergence in models with fixed network topologies or with independent
  • Coordination and Consensus of Networked Agents with Noisy Measurements . . .
    Abstract This paper considers the coordination and consensus of networked agents where each agent has noisy measurements of its neighbors’ states For consensus seeking, we propose stochastic approximation type algorithms with a decreasing step size, and introduce the notions of mean square and strong consensus Although the decreasing step size reduces the detrimental efiect of the noise
  • Stochastic Lyapunov Analysis for Consensus Algorithms with Noisy . . .
    For consensus seeking, we introduce stochastic approximation type algorithms with a decreasing step size We present a stochastic Lyaponuv analysis based upon the total mean potential asso-ciated with the agents
  • Stochastic Double Array Analysis and Convergence of Consensus . . .
    Abstract—This paper considers consensus-seeking of net-worked 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





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