Synchronization preserving model reduction of multi-agent network systems by eigenvalue assignments

Lanlin Yu, Xiaodong Cheng, Jacquelien M.A. Scherpen, Junlin Xiong

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

Abstract

In this paper, structure preserving model reduction problem for multi-agent network systems consisting of diffusively coupled agents is investigated. A new model reduction method based on eigenvalue assignment is derived. Particularly, the spectrum of the reduced Laplacian matrix is selected as a subset of the spectrum of the original Laplacian matrix. The resulting reduced-order model retains the network protocol of diffusive couplings, and thus the synchronization property is preserved. Moreover, a concise expression for the upper-bound of the 2 approximation error is presented in the setting of a leader-follower network, and it provides a guideline to select the eigenvalues of the reduced Laplacian matrix. The effectiveness of the proposed method is finally illustrated via the application to a spacecraft network, with a comparison of performances with the graph clustering method in [1] and balanced truncation approach in [2].

Original languageEnglish
Title of host publication2019 IEEE 58th Conference on Decision and Control, CDC 2019
Place of PublicationPiscataway
PublisherInstitute of Electrical and Electronics Engineers
Pages7794-7799
Number of pages6
ISBN (Electronic)9781728113982
DOIs
Publication statusPublished - 12 Mar 2020
Event58th IEEE Conference on Decision and Control (CDC 2019) - Nice, France
Duration: 11 Dec 201913 Dec 2019
https://cdc2019.ieeecss.org/

Conference

Conference58th IEEE Conference on Decision and Control (CDC 2019)
Abbreviated titleCDC 2019
CountryFrance
CityNice
Period11/12/1913/12/19
Internet address

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