Design of input assignment and feedback gain for re-stabilizing undirected networks with High-Dimension Low-Sample-Size data

Hitoshi Yasukata, Xun Shen, Hampei Sasahara*, Jun ichi Imura, Makito Oku, Kazuyuki Aihara

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

There exists a critical transition before dramatic deterioration of a complex dynamical system. Recently, a method to predict such shifts based on High-Dimension Low-Sample-Size (HDLSS) data has been developed. Thus based on the prediction, it is important to make the system more stable by feedback control just before such critical transitions, which we call re-stabilization. However, the re-stabilization cannot be achieved by traditional stabilization methods such as pole placement method because the available HDLSS data is not enough to get a mathematical system model by system identification. In this article, a model-free pole placement method for re-stabilization is proposed to design the optimal input assignment and feedback gain for undirected network systems only with HDLSS data. The proposed method is validated by numerical simulations.

Original languageEnglish
Pages (from-to)6734-6753
Number of pages20
JournalInternational Journal of Robust and Nonlinear Control
Volume33
Issue number12
DOIs
StatePublished - 2023/08

Keywords

  • dynamical network marker
  • network system
  • pole placement

ASJC Scopus subject areas

  • Control and Systems Engineering
  • General Chemical Engineering
  • Biomedical Engineering
  • Aerospace Engineering
  • Mechanical Engineering
  • Industrial and Manufacturing Engineering
  • Electrical and Electronic Engineering

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