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Full Description
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.
Contents
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.).
