Linear parameter-varying system identification : new developments and trends / editors, Paulo Lopes dos Santos ... [et al.].

Format:

Book

Contributor:

Santos, Paulo Lopes dos.

Series:

Advanced series in electrical and computer engineering ; v. 14.

Advanced series in electrical and computer engineering ; v. 14

Language:

English

Subjects (All):

Linear models (Statistics).

Physical Description:

1 online resource (402 p.)

Place of Publication:

Singapore : World Scientific Pub. Co., 2012.

Language Note:

English

Summary:

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 swi

Contents:

Contents; Preface; Acronyms; 1. Introduction C. Novara et al.; References; 2. Hybrid LPV Modeling and Identi.cation L. Giarr ́e, P. Falugi & R. Badalamenti; 1. Introduction; 2. Literature review on LPV identification; 2.1. I/O LPV Identi.cation [Bamieh and Giarr ́e (2002)]; 3. HLPV modeling: Problem formulation; 3.1. Hybrid LPV motivation; 3.2. I/O and SS model representations; 4. A motivating example: Tra.c modeling in wireless ad-hoc networks; 5. Final remarks and open problems; Acknowledgments; References

3. SM Identification of IO LPV Models with Uncertain Time- Varying Parameters V. Cerone, D. Piga & D. Regruto1. Introduction; 2. Problem formulation; 3. Evaluation of tight parameter bounds; 4. Semi-static LPV relaxation; 4.1. Overview of the relaxation procedure; 4.2. Technical results; 5. Properties of the computed parameter uncertaintyintervals PUI(n,δ); 6. Simulated example; 7. Conclusion; References; 4. SM Identification of State-Space LPV Systems C. Novara; 1. Introduction; 2. Notation and basic notions; 3. Set membership identi.cation of state-space LPV systems

4. Interpolatory and optimal estimates4.1. Identification algorithm; 4.2. Sparsity properties; 5. Important aspects of the identi.cation process; 5.1. Basis function choice; 5.2. Validation of prior assumptions and identification parameter choice; 5.3. Extension to MIMO systems; 6. Examples; 6.1. Example 1: Identification of the forced Van der Pol oscillator; 6.2. Example 2: Identification and control of a 2-DOF robot manipulator; 7. Conclusion; References; 5. Identification of Input-Output LPV Models V. Laurain et al.; 1. Introduction; 2. Discrete-time LPV polynomials models

2.1. LPV data generating system2.2. Polynomial LPV model; 2.2.1. Process model; 2.2.2. Noise model; 3. Estimating LPV-ARX models in; 3.1. LPV-ARX models; 3.2. The LS solution; 3.3. Non-white noise: An IV solution; 3.3.1. Instrumental variable method in the LTI case; 3.3.2. The optimal instrument for LPV-ARX models; 3.3.3. The LPV-IV4 method; 4. Addressing estimation with general noise models; 4.1. LPV-BJ model; 4.1.1. Noise model; 4.1.2. Whole model; 4.1.3. The LPV-BJ model issue; 4.1.4. Reformulation of the model equations; 4.2. A refined instrumental approach

4.2.1. Instrumental variable for LTI-BJ models4.2.2. The optimal instrument for LPV-BJ systems; 4.2.3. Iterative LPV-RIV algorithm for BJ models; 4.2.4. LPV-SRIV algorithm for OE models; 4.3. Examples; 4.3.1. Data-generating system; 4.3.2. Model assumptions; 4.3.3. Results; 4.4. LPV noise system example; 5. Direct estimation of continuous-time LPV systems; 5.1. System description; 5.2. Handling the time derivatives; 5.3. Hybrid models; 5.4. Hybrid LPV-BJ polynomial models; 6. Instrumental variable approach in continuous-time; 6.1. The optimal instrument for hybrid LPV-BJ systems

6.1.1. The LPV-RIVC algorithm for BJ models

Notes:

Description based upon print version of record.

Includes bibliographical references and index.

ISBN:

9786613646460

9781280669538

1280669535

9789814355452

9814355453

OCLC:

794328356