This paper presents a novel solution for the discrete time dynamic average consensus problem. Given a set of time-varying input signals over the nodes of an undirected graph, the proposed algorithm tracks, at each node, the input signals’ average. The algorithm is based on a sequence of consensus stages combined with a second order diffusive protocol. The former overcomes the need of k-th order differences of the inputs and conservation of the network state average, while the latter overcomes the trade-off between speed and accuracy of the consensus stages by just storing the previous estimate at each node. The result is a protocol that is fast, arbitrarily accurate, and robust against input noises and initializations. The protocol is extended to an asynchronous and randomized version that follows a gossiping scheme that is robust against potential delays and packet losses. We study the convergence properties of the algorithms and validate them via simulations.

Accelerated Multi-Stage Discrete Time Dynamic Average Consensus

Franceschelli, Mauro;Gasparri, Andrea
2023-01-01

Abstract

This paper presents a novel solution for the discrete time dynamic average consensus problem. Given a set of time-varying input signals over the nodes of an undirected graph, the proposed algorithm tracks, at each node, the input signals’ average. The algorithm is based on a sequence of consensus stages combined with a second order diffusive protocol. The former overcomes the need of k-th order differences of the inputs and conservation of the network state average, while the latter overcomes the trade-off between speed and accuracy of the consensus stages by just storing the previous estimate at each node. The result is a protocol that is fast, arbitrarily accurate, and robust against input noises and initializations. The protocol is extended to an asynchronous and randomized version that follows a gossiping scheme that is robust against potential delays and packet losses. We study the convergence properties of the algorithms and validate them via simulations.
2023
Consensus; distributed control; estimation; sensor networks
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11584/366263
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