Model Reduction of Multiagent Systems Using Dissimilarity-Based Clustering

Cheng, X., Kawano, Y. & Scherpen, J. M. A., Apr-2019, In : IEEE Transactions on Automatic Control. 64, 4, p. 1663-1670 8 p.

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  • Model Reduction of Multiagent Systems UsingDissimilarity-Based Clustering

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This technical note investigates a model reduction scheme for large-scale multiagent systems. The studied system is composed of identical linear subsystems interconnected by undirected weighted networks. To reduce the network complexity, a notion of nodal dissimilarity is established on the H-2-norms of transfer function deviations, and a new graph clustering algorithm is proposed to aggregate the pairs of nodes with smaller dissimilarities. The simplified system is verified to preserve an interconnection structure and the synchronization property. Moreover, a computable bound of the approximation error between the full-order and reduced-order models is provided, and the feasibility of the proposed approach is demonstrated by network examples.

Original languageEnglish
Pages (from-to)1663-1670
Number of pages8
JournalIEEE Transactions on Automatic Control
Issue number4
Publication statusPublished - Apr-2019


  • Graph clustering, model reduction, multiagent systems, synchronization, NETWORK SYSTEMS

ID: 80124663