Low-order model identification of MIMO systems from noisy and incomplete data

K. Bekiroglu, C. Lagoa, M. Sznaier

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

6 Scopus citations

Abstract

In this paper, we provide preliminary results aimed at solving the following problem: Given a priori information on Multi-Input/Multi-Output (MIMO) plant, namely constraints on the pole location, and scattered input/output data, find the lowest order model that is compatible with both the a priori assumptions and the collected data. By combining concepts from signal sparsification and subspace identification, algorithms are developed that can determine a low order model from data that is both corrupted by measurement noise and has missing measurements. Effectiveness of the proposed approach is shown by an academic example.

Original languageEnglish (US)
Title of host publication54rd IEEE Conference on Decision and Control,CDC 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages4029-4034
Number of pages6
ISBN (Electronic)9781479978861
DOIs
StatePublished - Feb 8 2015
Event54th IEEE Conference on Decision and Control, CDC 2015 - Osaka, Japan
Duration: Dec 15 2015Dec 18 2015

Publication series

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

Other

Other54th IEEE Conference on Decision and Control, CDC 2015
CountryJapan
CityOsaka
Period12/15/1512/18/15

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Modeling and Simulation
  • Control and Optimization

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