Abstract
| Language | English |
|---|---|
| Title of host publication | Control of linear parameter varying systems with applications |
| Editors | J. Mohammadpour, C. W. Scherer |
| Place of Publication | Heidelberg |
| Publisher | Springer |
| Pages | 27-60 |
| Number of pages | 547 |
| ISBN (Print) | 978-1-4614-1833-7 |
| DOIs | |
| State | Published - 2012 |
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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 proceeding › Chapter › Academic
TY - CHAP
T1 - Prediction-error identification of LPV systems : present and beyond
AU - Toth,R.
AU - Heuberger,P.S.C.
AU - Hof, Van den,P.M.J.
PY - 2012
Y1 - 2012
N2 - 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.
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.
U2 - 10.1007/978-1-4614-1833-7_2
DO - 10.1007/978-1-4614-1833-7_2
M3 - Chapter
SN - 978-1-4614-1833-7
SP - 27
EP - 60
BT - Control of linear parameter varying systems with applications
PB - Springer
CY - Heidelberg
ER -