Del 14 i serien Advanced Series in Electrical & Computer Engineering

Linear Parameter-varying System Identification: New Developments And Trends

Inbunden, Engelska, 2012

AvAl Paulo Lopes Dos Santos Et,Paulo Lopes Dos Santos,Teresa Paula Azevedo Perdicoulis,Carlo Novara,Jose A Ramos,Danel E Rivera

2 659 kr

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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.

Produktinformation

Utgivningsdatum2012-01-04
Mått159 x 236 x 28 mm
Vikt704 g
FormatInbunden
SpråkEngelska
SerieAdvanced Series in Electrical & Computer Engineering
Antal sidor404
FörlagWorld Scientific Publishing Co Pte Ltd
ISBN9789814355445

Tillhör följande kategorier

An Unified Framework for LPV, Switching and Affine Models Identification (B Bamieh et al.); Set-Membership Identification of LPV Models with Uncertain Time-Varying Parameters (V Cerone et al.); Set Membership Identification of State Space LPV Systems (C Novara); Identification of Discrete-Time and Continuous-Time Input/Output LPV Models (V Laurain et al.); Reducing the Dimensions of Data Matrices Involved in LPV Subspace Identification Methods (V Verdult & M Verhaegen); An Open Loop and Closed Loop LPV Subspace Identification Algorithm (J-W van Wingerden & M Verhaegen); Subspace Identification of Continuous-Time State-Space LPV Models (M Bergamasco & M Lovera); Identification of Continuous-Time LPV Systems Using the Subspace Successive Approximations Algorithm (P L dos Santos et al.); LPV Identification using Series-Expansion Models (R Toth et al.); Expectation Maximization and Gradient Methods for LPV State-Space Models Identification (A Wills et al.); Piecewise Affine Identification of Interconnected Systems with LFR Structure (S Paoletti & A Garulli); Identification and Model (In)validation of Switched Affine Systems (C Feng et al.).