In this paper we propose a novel local interaction protocol which solves the discrete time dynamic average consensus problem, i.e., the consensus problem on the average value of a set of time-varying input signals in an undirected graph. The proposed interaction protocol is based on a multi-stage cascade of consensus filters which tracks the average value of the inputs with small error. We characterize how the number of stages influences the steady state error. The main novelty of the proposed algorithm is that, with respect to other dynamic average consensus protocols, we do not exploit the k-th order derivatives of the inputs nor we require that the average of the network state is preserved to achieve convergence to the desired quantity, thus increasing the robustness of the method in several practical scenarios. In addition, the proposed design allows to trade-off convergence time with steady-state error by choosing a proper number of stages in the cascade. Finally, we provide a preliminary asynchronous and randomized version of the proposed protocol along with numerical examples to corroborate the theoretical findings.

Franceschelli, M., Gasparri, A. (2016). Multi-stage discrete time dynamic average consensus. In 2016 IEEE 55th Conference on Decision and Control, CDC 2016 (pp.897-903). Institute of Electrical and Electronics Engineers Inc. [10.1109/CDC.2016.7798381].

Multi-stage discrete time dynamic average consensus

GASPARRI, ANDREA
2016-01-01

Abstract

In this paper we propose a novel local interaction protocol which solves the discrete time dynamic average consensus problem, i.e., the consensus problem on the average value of a set of time-varying input signals in an undirected graph. The proposed interaction protocol is based on a multi-stage cascade of consensus filters which tracks the average value of the inputs with small error. We characterize how the number of stages influences the steady state error. The main novelty of the proposed algorithm is that, with respect to other dynamic average consensus protocols, we do not exploit the k-th order derivatives of the inputs nor we require that the average of the network state is preserved to achieve convergence to the desired quantity, thus increasing the robustness of the method in several practical scenarios. In addition, the proposed design allows to trade-off convergence time with steady-state error by choosing a proper number of stages in the cascade. Finally, we provide a preliminary asynchronous and randomized version of the proposed protocol along with numerical examples to corroborate the theoretical findings.
9781509018376
Franceschelli, M., Gasparri, A. (2016). Multi-stage discrete time dynamic average consensus. In 2016 IEEE 55th Conference on Decision and Control, CDC 2016 (pp.897-903). Institute of Electrical and Electronics Engineers Inc. [10.1109/CDC.2016.7798381].
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11590/315674
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 7
  • ???jsp.display-item.citation.isi??? 4
social impact