A Novel Sequence Weighting Method for First-Order Consensus Problems

Publikationstyp
Conference Paper
Date Issued
2019-01-18
Sprache
English
Author(s)
Mirali, Furugh  
Werner, Herbert  
Institut
Regelungstechnik E-14  
TORE-URI
http://hdl.handle.net/11420/2195
Journal
Proceedings of the IEEE Conference on Decision & Control  
Start Page
97
End Page
102
Citation
Proceedings of the IEEE Conference on Decision and Control (2018-December): 97-102 (2019-01-18)
Contribution to Conference
57th IEEE Conference on Decision and Control, CDC 2018  
Publisher DOI
10.1109/CDC.2018.8619131
Scopus ID
2-s2.0-85062195176
In this paper we present a novel method for constructing stochastic weighting matrices with the help of a finite sequence that can be chosen according to the application in a distributed manner. In addition, we propose three algorithms that determine how every agent decides on assigning these weights to its neighbours. Then, the so-called sequence weighting method is compared with other existing approaches for the special case of a one-dimensional lattice graph. For this purpose, we derive the characteristic polynomial of a quasi- Toeplitz matrix. Considering the sequence weighting method we calculate a bound for the second greatest eigenvalue that can be bounded away from 1 independent of the network size. Using a recently reported result about uniform packet loss, we show that bounds on the convergence speed not only hold in the loss-free case, but also when uniform packet loss occurs. Simulation results with non-uniform packet loss confirm a better performance using the sequence weighting method in comparison to existing strategies. © 2018 IEEE.