Linear parameter-varying system identification : new developments and trends
Author(s)
Bibliographic Information
Linear parameter-varying system identification : new developments and trends
（Advanced series in electrical and computer engineering, v. 14）
World Scientific, c2012
Available at / 1 libraries
-
No Libraries matched.
- Remove all filters.
Note
Includes bibliographical references and index
Other editors: Teresa Paula Azevedo Perdicoúlis, Carlo Novara, Jose A Ramos, Daniel E Rivera
Description and Table of Contents
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.
Table of 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.).
by "Nielsen BookData"