Order and structural dependence selection of LPV-ARX models using a nonnegative Garrote approach

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Abstract

In order to accurately identify linear parameter-varying (LPV) systems, order selection of LPV linear regression models has prime importance. Existing identification approaches in this context suffer from the drawback that a set of functional dependencies needs to be chosen a priori for the parametrization of the model coefficients. However in a black-box setting, it has not been possible so far to decide which functions from a given set are required for the parametrization and which are not. To provide a practical solution, a nonnegative garrote approach is applied. It is shown that using only a measured data record of the plant, both the order selection and the selection of structural coefficient dependence can be solved by the proposed method.
Original languageEnglish
Title of host publicationProceedings of the 48th IEEE Conference on Decision and Control (CDC 2009), 16-18 December 2009, Shanghai, China
Place of PublicationPiscataway
PublisherInstitute of Electrical and Electronics Engineers
Pages7406-7411
ISBN (Print)978-1-4244-3871-6
DOIs
Publication statusPublished - 2009
Event48th IEEE Conference on Decision and Control (CDC 2009) - "Shanghai International Convention Center", Shanghai, China
Duration: 16 Dec 200918 Dec 2009
Conference number: 48
http://people.bu.edu/johnb/CDC2009-cfp.pdf

Conference

Conference48th IEEE Conference on Decision and Control (CDC 2009)
Abbreviated titleCDC 2009
CountryChina
CityShanghai
Period16/12/0918/12/09
Internet address

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    Toth, R., Lyzell, C., Enqvist, M., Heuberger, P. S. C., & Hof, Van den, P. M. J. (2009). Order and structural dependence selection of LPV-ARX models using a nonnegative Garrote approach. In Proceedings of the 48th IEEE Conference on Decision and Control (CDC 2009), 16-18 December 2009, Shanghai, China (pp. 7406-7411). Piscataway: Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/CDC.2009.5399551