Structure-Based Clustering Algorithm for Model Reduction of Large-Scale Network Systems (I)

Niazi, M. U. B., Cheng, X., Canudas de Wit, C. & Scherpen, J. M. A., 2019, Proceedings of the 58th Conference on Decision and Control.

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

A model reduction technique is presented that identifies and aggregates clusters in a large-scale network system and yields a reduced model with tractable dimension. The network clustering problem is translated to a graph reduction problem, which is formulated as a minimization of distance from lumpability. The problem is a non-convex, mixed-integer optimization problem and only depends on the graph structure of the system. We provide a heuristic algorithm to identify clusters that are not only suboptimal but are also connected, that is, each cluster forms a connected induced subgraph in the network system.
Original languageEnglish
Title of host publicationProceedings of the 58th Conference on Decision and Control
Publication statusPublished - 2019
Event58th Conference on Decision and Control (CDC2019) - Nice, France
Duration: 11-Dec-201913-Dec-2019


Conference58th Conference on Decision and Control (CDC2019)


58th Conference on Decision and Control (CDC2019)


Nice, France

Event: Conference

ID: 98549217