Options
A Dynamic Quasi-Taylor Approach for Distributed Consensus Problems with Packet Loss
Publikationstyp
Conference Paper
Date Issued
2020-07
Sprache
English
Author(s)
Institut
TORE-URI
Volume
2020-July
Start Page
701
End Page
706
Article Number
9147682
Citation
American Control Conference (ACC 2020)
Contribution to Conference
Publisher DOI
Scopus ID
This paper presents a novel approach for handling packet loss in first-order consensus protocols based on a Taylor series expansion. We propose a dynamic memory approach which depends on a locally measured loss rate at each agent. The quasi-Taylor method assumes that each agent is storing not only the past received value of its neighbours in a memory, but the last ν received states of all neighbours in order to predict the future trajectory with the quasi-Taylor estimation. In addition, we use the so-called importance measure to label the most important information received at each time step. Then, depending on the measured loss rate the past data points of a neighbour are used to predict the future trajectory. Hereby, the trajectory is determined as a convex combination of different orders of the quasi-Taylor estimation. In order to minimise the distance of the consensus value to the actual average, we propose to use an adaptive step size for predicting the future trajectory of the neighbours. We give an upper bound on the convergence rate when uniform packet loss is assumed and show that the proposed approach outperforms existing methods from the literature with the help of simulation studies.