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

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

Onderzoeksoutput: Hoofdstuk in Boek/Rapport/CongresprocedureConferentiebijdrageAcademicpeer review


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].

Originele taal-2Engels
Titel2019 IEEE 58th Conference on Decision and Control, CDC 2019
Plaats van productiePiscataway
UitgeverijInstitute of Electrical and Electronics Engineers
Aantal pagina's6
ISBN van elektronische versie9781728113982
StatusGepubliceerd - 12 mrt 2020
Evenement58th IEEE Conference on Decision and Control (CDC 2019) - Nice, Frankrijk
Duur: 11 dec 201913 dec 2019


Congres58th IEEE Conference on Decision and Control (CDC 2019)
Verkorte titelCDC 2019
Internet adres

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