Prediction-error identification of LPV systems : present and beyond

Research output: Chapter in Book/Report/Conference proceedingChapterAcademic

15 Citations (Scopus)

Abstract

The proposed chapter aims at presenting a unified framework of prediction-error based identification of LPV systems using freshly developed theoretical results. Recently, these methods have got a considerable attention as they have certain advantages in terms of computational complexity, optimality in the stochastic sense and available theoretical tools to analyze estimation errors like bias, variance, etc., and the understanding of consistency and convergence. Beside the introduction of the theoretical tools and the prediction-error framework itself,the scope of the chapter includes a detailed investigation of the LPV extension of the classical model structures like ARX, ARMAX, Box–Jenkins, OE, FIR, and series expansion models, like orthonormal basis functions based structures, together with their available estimation approaches including linear regression, nonlinear optimization, and iterative IV methods. Questions of model structure selection and experimental design are also investigated. In this way, the chapter provides a detailed overview about the state-of-the-art of LPV prediction-error identification giving the reader an easy guide to find the right tools of his LPV identification problems.
LanguageEnglish
Title of host publicationControl of linear parameter varying systems with applications
EditorsJ. Mohammadpour, C. W. Scherer
Place of PublicationHeidelberg
PublisherSpringer
Pages27-60
Number of pages547
ISBN (Print)978-1-4614-1833-7
DOIs
StatePublished - 2012

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Model structures
Linear regression
Design of experiments
Error analysis
Computational complexity

Cite this

Toth, R., Heuberger, P. S. C., & Hof, Van den, P. M. J. (2012). Prediction-error identification of LPV systems : present and beyond. In J. Mohammadpour, & C. W. Scherer (Eds.), Control of linear parameter varying systems with applications (pp. 27-60). Heidelberg: Springer. DOI: 10.1007/978-1-4614-1833-7_2
Toth, R. ; Heuberger, P.S.C. ; Hof, Van den, P.M.J./ Prediction-error identification of LPV systems : present and beyond. Control of linear parameter varying systems with applications. editor / J. Mohammadpour ; C. W. Scherer. Heidelberg : Springer, 2012. pp. 27-60
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Toth, R, Heuberger, PSC & Hof, Van den, PMJ 2012, Prediction-error identification of LPV systems : present and beyond. in J Mohammadpour & CW Scherer (eds), Control of linear parameter varying systems with applications. Springer, Heidelberg, pp. 27-60. DOI: 10.1007/978-1-4614-1833-7_2

Prediction-error identification of LPV systems : present and beyond. / Toth, R.; Heuberger, P.S.C.; Hof, Van den, P.M.J.

Control of linear parameter varying systems with applications. ed. / J. Mohammadpour; C. W. Scherer. Heidelberg : Springer, 2012. p. 27-60.

Research output: Chapter in Book/Report/Conference proceedingChapterAcademic

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AB - The proposed chapter aims at presenting a unified framework of prediction-error based identification of LPV systems using freshly developed theoretical results. Recently, these methods have got a considerable attention as they have certain advantages in terms of computational complexity, optimality in the stochastic sense and available theoretical tools to analyze estimation errors like bias, variance, etc., and the understanding of consistency and convergence. Beside the introduction of the theoretical tools and the prediction-error framework itself,the scope of the chapter includes a detailed investigation of the LPV extension of the classical model structures like ARX, ARMAX, Box–Jenkins, OE, FIR, and series expansion models, like orthonormal basis functions based structures, together with their available estimation approaches including linear regression, nonlinear optimization, and iterative IV methods. Questions of model structure selection and experimental design are also investigated. In this way, the chapter provides a detailed overview about the state-of-the-art of LPV prediction-error identification giving the reader an easy guide to find the right tools of his LPV identification problems.

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Toth R, Heuberger PSC, Hof, Van den PMJ. Prediction-error identification of LPV systems : present and beyond. In Mohammadpour J, Scherer CW, editors, Control of linear parameter varying systems with applications. Heidelberg: Springer. 2012. p. 27-60. Available from, DOI: 10.1007/978-1-4614-1833-7_2