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Order and structural dependence selection of LPV-ARX models revisited
Department of Electrical Engineering, Eindhoven University of Technology.
KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre. (System Identification Group)ORCID iD: 0000-0002-9368-3079
KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre. (System Identification Group)ORCID iD: 0000-0003-0355-2663
2012 (English)In: Decision and Control (CDC), 2012 IEEE 51st Annual Conference on, IEEE , 2012, 6271-6276 p.Conference paper, Published paper (Refereed)
Abstract [en]

Accurate parametric identification of Linear Parameter-Varying (LPV) systems requires an optimal prior selection of model order and a set of functional dependencies for the parameterization of the model coefficients. In order to address this problem for linear regression models, a regressor shrinkage method, the Non-Negative Garrote (NNG) approach, has been proposed recently. This approach achieves statistically efficient order and structural coefficient dependence selection using only measured data of the system. However, particular drawbacks of the NNG are that it is not applicable for large-scale over-parameterized problems due to computational limitations and that adequate performance of the estimator requires a relatively large data set compared to the size of the parameterization used in the model. To overcome these limitations, a recently introduced L1 sparse estimator approach, the so-called SPARSEVA method, is extended to the LPV case and its performance is compared to the NNG.

Place, publisher, year, edition, pages
IEEE , 2012. 6271-6276 p.
Series
IEEE Conference on Decision and Control. Proceedings, ISSN 0191-2216
Keyword [en]
ARX model, compressive system identification, identification, Linear parameter-varying systems, order selection, sparse estimators
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:kth:diva-104648DOI: 10.1109/CDC.2012.6426552Scopus ID: 2-s2.0-84874235701OAI: oai:DiVA.org:kth-104648DiVA: diva2:565705
Conference
51st IEEE Conference on Decision and Control, CDC 2012;Maui, HI;10 December 2012 through 13 December 2012
Funder
ICT - The Next Generation
Note

QC 20130304

Available from: 2012-11-08 Created: 2012-11-08 Last updated: 2013-04-15Bibliographically approved

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Hjalmarsson, HåkanRojas, Cristian R.

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