Accelerated Multi-Stage Discrete Time Dynamic Average Consensus

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

This letter 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

On Robustness Analysis of a Dynamic Average Consensus Algorithm to Communication Delay

This paper studies the robustness of a dynamic average consensus algorithm to communication delay over strongly connected and weight-balanced (SCWB) digraphs. Under delay-free communication, the algorithm of interest achieves a practical asymptotic tracking of the dynamic average of the time-varying agents' reference signals. For this algorithm, in both its continuous-time and discrete-time implementations, we characterize the admissible communication delay range and study the effect of the delay on the rate of convergence and the tracking error bound. Our study also includes establishing a relationship between the admissible delay bound and the maximum degree of the SCWB digraphs. We also show that for delays in the admissible bound, for static signals the algorithms achieve perfect tracking. Moreover, when the interaction topology is a connected undirected graph, we show that the discrete-time implementation is guaranteed to tolerate at least one step delay. Simulations demonstrate our results