Linear Parameter-Varying System Identification: New Developments and Trends

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World Scientific, 2012 - Discrete-time systems - 402 pages
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This review volume reports the state-of-the-art in Linear Parameter Varying (LPV) system identification. Written by world renowned researchers, the book contains twelve chapters, focusing on the most recent LPV identification methods for both discrete-time and continuous-time models, using different approaches such as optimization methods for input/output LPV models Identification, set membership methods, optimization methods and subspace methods for state-space LPV models identification and orthonormal basis functions methods. Since there is a strong connection between LPV systems, hybrid switching systems and piecewise affine models, identification of hybrid switching systems and piecewise affine systems will be considered as well.
 

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Contents

1 Introduction C Novara et al
1
2 Hybrid LPV Modeling and Identication L Giarre P Falugi R Badalamenti
11
3 SM Identification of IO LPV Models with Uncertain Time Varying Parameters V Cerone D Piga D Regruto
41
4 SM Identification of StateSpace LPV Systems C Novara
65
5 Identification of InputOutput LPV Models V Laurain et al
95
6 Reducing the Dimensions of Data Matrices in LPV Subspace Identification V Verdult M Verhaegen
133
7 Subspace Identification of MIMO LPV Systems J W van Wingerden M Verhaegen
167
8 Subspace Identification of ContinuousTime StateSpace LPV Models M Bergamasco M Lovera
201
9 Indirect ContinuousTime LPV System Identication P Lopes dos Santos et al
231
10 LPV System Identication Using Series Expansion Models R Toth P S C Heuberger P M J Van den Hof
259
11 System Identification of Linear Parameter Varying State Space Models A Wills B Ninness
295
12 PWA Identification of Interconnected Systems with LFR Structure S Paoletti A Garulli
317
13 Identification and Model Invalidation of Switched ARX Systems C Feng et al
347
Index
381
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