LPV system identification with globally fixed orthonormal basis functions

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Abstract

A global and a local identification approach are developed for approximation of linear parameter-varying (LPV) systems. The utilized model structure is a linear combination of globally fixed (scheduling-independent) orthonormal basis functions (OBFs) with scheduling-parameter dependent weights. Whether the weighting is applied on the input or on the output side of the OBFs, the resulting models have different modeling capabilities. The local identification approach of these structures is based on the interpolation of locally identified LTI models on the scheduling domain where the local models are composed from a fixed set of OBFs. The global approach utilizes a priori chosen functional dependence of the parameter-varying weighting of a fixed set of OBFs to deliver global model estimation from measured I/O data. Selection of the OBFs that guarantee the least worst-case modeling error for the local behaviors in an asymptotic sense, is accomplished through the fuzzy Kolmogorov c-max approach. The proposed methods are analyzed in terms of applicability and consistency of the estimates.
Original languageEnglish
Title of host publicationProceedings of the 46th Conference on Decision and Control (CDC 2007) 12-14 December 2007, New Orleans, Louisiana
Place of PublicationPiscataway
PublisherInstitute of Electrical and Electronics Engineers
Pages3646-3653
ISBN (Print)978-1-4244-1497-0
DOIs
Publication statusPublished - 2007
Event46th IEEE Conference on Decision and Control (CDC 2007) - New Orleans, United States
Duration: 12 Dec 200714 Dec 2007
Conference number: 46

Conference

Conference46th IEEE Conference on Decision and Control (CDC 2007)
Abbreviated titleCDC 2007
Country/TerritoryUnited States
CityNew Orleans
Period12/12/0714/12/07

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