Clustered model reduction of interconnected second-order systems

• Ishizaki Takayuki

  Graduate School of Information Science and Engineering, Tokyo Institute of Technology CREST, Japan Science and Technology Agency

• Imura Jun-ichi

  Graduate School of Information Science and Engineering, Tokyo Institute of Technology CREST, Japan Science and Technology Agency

Abstract

In this paper, we propose a clustered model reduction method for interconnected second-order systems evolving over undirected networks, which we call second-order networks. In this model reduction method, network clustering, i.e., clustering of subsystems, is performed according to cluster reducibility, which is defined as a notion of weak controllability of local subsystem states. This paper clarifies that the cluster reducibility can be algebraically characterized for second-order networks through the controller-Hessenberg transformation of their first-order representation. By aggregating the reducible clusters, we obtain an approximate model that preserves an interconnection topology among clustered subsystems. Furthermore, we derive an H_∞-error bound of the state discrepancy caused by the cluster aggregation. Finally, the efficiency of the proposed method is demonstrated through an example of large-scale complex networks.

Journal

• Nonlinear Theory and Its Applications, IEICE

  Nonlinear Theory and Its Applications, IEICE 6 (1), 26-37, 2015

  The Institute of Electronics, Information and Communication Engineers

Keywords

• clustered model reduction
• interconnected second-order systems
• complex networks
• network clustering

Details

• CRID: 1390282680322305408
• NII Article ID: 130004960157
• DOI: 10.1587/nolta.6.26
• ISSN: 21854106
• Web Site: https://www.jstage.jst.go.jp/article/nolta/6/1/6_26/_pdf
• Text Lang: en
• Data Source:
  • JaLC
  • Crossref
  • CiNii Articles
• Abstract License Flag: Disallowed