Model structures for identification of linear parameter-varying (LPV) models

P.M.J. Hof, Van den, R. Toth, P.S.C. Heuberger

Research output: Chapter in Book/Report/Conference proceedingChapterAcademic

71 Downloads (Pure)


Describing nonlinear dynamic systems by linear parameter-varying models has become an attractive tool for control of complex systems with regimedependent (linear) behavior. For the identification of LPV models from experimental data a number of methods has been presented in the literature but a full picture of the underlying identification problem is still missing. In this contribution a solid system theoretic basis for the description of model structures for LPV models is presented, together with a general approach to the LPV identification problem. Use is made of a series expansion approach to LPV modeling, employing orthogonal basis function expansions.
Original languageEnglish
Title of host publicationProceedings of the Workshop on Systems and Control Theory in honor of József Bokor on his 60th Birthday
EditorsK.M. Hangos, L. Nádai
Place of PublicationBudapest
ISBN (Print)978-963-279-039-8
Publication statusPublished - 2009


Dive into the research topics of 'Model structures for identification of linear parameter-varying (LPV) models'. Together they form a unique fingerprint.

Cite this