Structure-based Clustering Algorithm for Model Reduction of Large-scale Network Systems

被引：0

作者：

Niazi, Muhammad Umar B. ^{[1
]}

Chen, Xiaodong ^{[2
]}

Canudas-de-Wit, Carlos ^{[1
]}

Scherpen, Jacquelien M. A. ^{[3
]}

机构：

[1] CNRS, GIPSA Lab, Grenoble, France

[2] Eindhoven Univ Technol, Dept Elect Engn, Control Syst Grp, Eindhoven, Netherlands

[3] Univ Groningen, Fac Sci & Engn, Jan C Willems Ctr Syst & Control, Engn & Technol Inst, Groningen, Netherlands

来源：

2019 IEEE 58TH CONFERENCE ON DECISION AND CONTROL (CDC) | 2019年

基金：

欧洲研究理事会;

关键词：

Large-scale systems; model reduction; lumpability; clustering algorithm;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

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.

引用

页码：5038 / 5043

页数：6

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