Identification of structured LTI MIMO state-space models

C Yu, M Verhaegen, S Kovalsky, R Basri

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

5 Citations (Scopus)
157 Downloads (Pure)

Abstract

The identification of structured state-space model has been intensively studied for a long time but still has not been adequately addressed. The main challenge is that the involved estimation problem is a non-convex (or bilinear) optimization problem. This paper is devoted to developing an identification
method which aims to find the global optimal solution under mild computational burden. Key to the developed identification algorithm is to transform a bilinear estimation to a rank constrained optimization problem and further a difference of convex programming (DCP) problem. The initial condition
for the DCP problem is obtained by solving its convex part of the optimization problem which happens to be a nuclear norm regularized optimization problem. Since the nuclear norm regularized optimization is the closest convex form of the low-rank constrained estimation problem, the obtained initial
condition is always of high quality which provides the DCP problem a good starting point. The DCP problem is then solved by the sequential convex programming method. Finally, numerical examples are included to show the effectiveness of the developed identification algorithm.
Original languageEnglish
Title of host publicationProceedings of the 2015 IEEE 54th Annual Conference on Decision and Control
EditorsME Valcher, Y Ohta, M Sampei
Place of PublicationPiscataway, NJ, USA
PublisherIEEE Society
Pages2737-2742
ISBN (Print)978-1-4799-7884-7
DOIs
Publication statusPublished - 2015
Event54th IEEE Conference on Decision and Control, CDC 2015 - Osaka, Japan
Duration: 15 Dec 201518 Dec 2015
Conference number: 54

Conference

Conference54th IEEE Conference on Decision and Control, CDC 2015
Abbreviated titleCDC 2015
Country/TerritoryJapan
CityOsaka
Period15/12/1518/12/15

Bibliographical note

Accepted Author Manuscript

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