eprintid: 2448
rev_number: 9
eprint_status: archive
userid: 6
dir: disk0/00/00/24/48
datestamp: 2015-01-09 11:59:20
lastmod: 2015-01-09 11:59:20
status_changed: 2015-01-09 11:59:20
type: book_section
metadata_visibility: show
creators_name: Cerone, Vito
creators_name: Piga, Dario
creators_name: Regruto, Diego
creators_name: Tóth, Roland
creators_id: 
creators_id: dario.piga@imtlucca.it
creators_id: 
creators_id: 
title: Input-Output LPV Model identification with guaranteed quadratic stability
ispublished: pub
subjects: QA75
divisions: CSA
full_text_status: public
keywords: LPV system; quadratic stability; polynomial optimization; convex relaxation
note: 16th IFAC Symposium on System Identification held in Brussels (Belgium), 11-13 July 2012
abstract: The problem of identifying linear parameter-varying (LPV) systems, a-priori known to be quadratically stable, is considered in the paper using an input-output model structure. To solve this problem, a novel constrained optimization-based algorithm is proposed which guarantees quadratic stability of the identified model. It is shown that this estimation objective corresponds to a nonconvex optimization problem, defined by a set of polynomial matrix inequalities (PMI), whose optimal solution can be approximated by means of suitable convex semidefinite relaxations. Applicability of such relaxation-based estimation approach in the presence of either stochastic or deterministic bounded noise is discussed. A simulation example is also given to demonstrate the effectiveness of the resulting identification method.
date: 2012-07
date_type: published
volume: 16
publisher: IFAC
pagerange: 1767-1772
id_number: 10.3182/20120711-3-BE-2027.00242
refereed: TRUE
isbn: 978-3-902823-06-9
book_title: 16th IFAC Symposium on System Identification
official_url: http://www.ifac-papersonline.net/Detailed/55021.html
citation:   Cerone, Vito and Piga, Dario and Regruto, Diego and Tóth, Roland  Input-Output LPV Model identification with guaranteed quadratic stability.   In:  16th IFAC Symposium on System Identification.   IFAC, pp. 1767-1772.  ISBN 978-3-902823-06-9      (2012)  
document_url: http://eprints.imtlucca.it/2448/1/IFAC2012PreprintPiga.pdf