Linear parameter-varying system identification : new developments and trends

著者

    • Lopes dos Santos, Paulo
    • Azevedo Perdicoúlis, Teresa Paula
    • Novara, Carlo
    • Ramos, Jose A
    • Rivera, Daniel E

書誌事項

Linear parameter-varying system identification : new developments and trends

editors, Paulo Lopes dos Santos ... [et al.]

（Advanced series in electrical and computer engineering, v. 14）

World Scientific, c2012

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注記

Includes bibliographical references and index

Other editors: Teresa Paula Azevedo Perdicoúlis, Carlo Novara, Jose A Ramos, Daniel E Rivera

内容説明・目次

内容説明

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.

目次

  • 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.).

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