Moment-constrained subspace identification using a priori knowledge

Masaki Inoue, Ayaka Matsubayashi, Shuichi Adachi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Citations (Scopus)


This paper proposes a subspace identification method involving a priori knowledge characterized as the moment of the transfer function. First, it is shown that any moment defined on the complex plane is expressed in terms of the solution to a Sylvester matrix equation with real-valued coefficients. Then, incorporating the Sylvester equation with the conventional subspace method, we formulate a moment-constrained subspace identification problem. Application of a proper weight reduces the constrained identification problem to a non-constrained quadratic programming. Finally, we propose a two-stage identification procedure: First, in the preliminary identification stage, the moments are estimated by using a specific input signal. Then, in the main identification stage, a state-space model is constructed based on the proposed identification method using the measured input-output data and the estimated moments. The effectiveness of the two-stage procedure is shown in a numerical simulation.

Original languageEnglish
Title of host publication54rd IEEE Conference on Decision and Control,CDC 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages6
ISBN (Electronic)9781479978861
Publication statusPublished - 2015 Feb 8
Event54th IEEE Conference on Decision and Control, CDC 2015 - Osaka, Japan
Duration: 2015 Dec 152015 Dec 18

Publication series

NameProceedings of the IEEE Conference on Decision and Control
Volume54rd IEEE Conference on Decision and Control,CDC 2015
ISSN (Print)0743-1546
ISSN (Electronic)2576-2370


Other54th IEEE Conference on Decision and Control, CDC 2015

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Modelling and Simulation
  • Control and Optimization


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