We propose a class of distributed algorithms for computing arithmetic averages (average consensus) overnetworks of agents connected through digital noisy broadcast channels. Our algorithms do not require the agents t...
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We propose a class of distributed algorithms for computing arithmetic averages (average consensus) overnetworks of agents connected through digital noisy broadcast channels. Our algorithms do not require the agents to have knowledge of the network structure, nor do they assume any noiseless feedback to be available. We prove convergence to consensus, with both number of channel uses and computational complexity which are poly-logarithmic in the desired precision.
We consider a distributed tracking problem where agents interact locally with limited information. Each agent maintains both a discrete value and an estimate of the mean of that value taken over all agents. In earlier...
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We consider a distributed tracking problem where agents interact locally with limited information. Each agent maintains both a discrete value and an estimate of the mean of that value taken over all agents. In earlier work, we designed an estimator that converged to the desired value with a finite variance and here, we derive a different estimator with zero variance. We design the controller and estimator separately, prove their simultaneous convergence and stability, finally demonstrate the results in simulation. While we present this work in the context of of stochastic self-assembly, the algorithm can be applied other settings.
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