Speeding up finite-time consensus via minimal polynomial of a weighted graph — A numerical approach

Speeding up finite-time consensus via minimal polynomial of a weighted graph — A numerical... This work proposes an approach to speed up finite-time consensus algorithm using the weights of a weighted Laplacian matrix. It is motivated by the need to reach consensus among states of a multi-agent system in a distributed control/optimization setting. The approach is an iterative procedure that finds a low-order minimal polynomial that is consistent with the topology of the underlying graph. In general, the lowest-order minimal polynomial achievable for a network system is an open research problem. This work proposes a numerical approach that searches for the lowest order minimal polynomial via a rank minimization problem using a two-step approach: the first being an optimization problem involving the nuclear norm and the second a correction step. Convergence of the algorithm is shown and effectiveness of the approach is demonstrated via several examples. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Automatica Elsevier

Speeding up finite-time consensus via minimal polynomial of a weighted graph — A numerical approach

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Publisher
Elsevier
Copyright
Copyright © 2018 Elsevier Ltd
ISSN
0005-1098
D.O.I.
10.1016/j.automatica.2018.03.067
Publisher site
See Article on Publisher Site

Abstract

This work proposes an approach to speed up finite-time consensus algorithm using the weights of a weighted Laplacian matrix. It is motivated by the need to reach consensus among states of a multi-agent system in a distributed control/optimization setting. The approach is an iterative procedure that finds a low-order minimal polynomial that is consistent with the topology of the underlying graph. In general, the lowest-order minimal polynomial achievable for a network system is an open research problem. This work proposes a numerical approach that searches for the lowest order minimal polynomial via a rank minimization problem using a two-step approach: the first being an optimization problem involving the nuclear norm and the second a correction step. Convergence of the algorithm is shown and effectiveness of the approach is demonstrated via several examples.

Journal

AutomaticaElsevier

Published: Jul 1, 2018

References

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