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

3 Citations (Scopus)
36 Downloads (Pure)


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 publicationProceedings of the 58th Conference on Decision and Control
Number of pages6
ISBN (Electronic)978-1-7281-1398-2
ISBN (Print)978-1-7281-1399-9
Publication statusPublished - Dec-2019
Event58th Conference on Decision and Control (CDC2019) - Nice, France
Duration: 11-Dec-201913-Dec-2019


Conference58th Conference on Decision and Control (CDC2019)


  • Model/Controller reduction, Networked control systems, Network analysis and control

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