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E-raamat: Linear Parameter-varying System Identification: New Developments And Trends

Edited by (Univ De Tras-os-montes E Alto Douro, Portugal), Edited by (Arizona State Univ, Usa), Edited by (Politecnico Di Torino, Italy), Edited by (Univ Do Porto, Portugal), Edited by (Nova Southeastern Univ, Usa)
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Linear Parameter-varying System Identification: New Developments And TrendsSuurem pilt
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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.
Preface v
Acronyms xiii
1 Introduction
1(10)
C. Novara
References
6(5)
2 Hybrid LPV Modeling and Identification
11(30)
L. Giarre
P. Falugi
R. Badalamenti
1 Introduction
11(2)
2 Literature review on LPV identification
13(7)
3 HLPV modeling: Problem formulation
20(3)
4 A motivating example: Traffic modeling in wireless ad-hoc networks
23(9)
5 Final remarks and open problems
32(2)
References
34(7)
3 SM Identification of IO LPV Models with Uncertain Time-Varying Parameters
41(24)
V. Cerone
D. Piga
D. Regruto
1 Introduction
41(3)
2 Problem formulation
44(1)
3 Evaluation of tight parameter bounds
45(2)
4 Semi-static LPV relaxation
47(10)
5 Properties of the computed parameter uncertainty intervals PUI(n, δ) j
57(3)
6 Simulated example
60(2)
7 Conclusion
62(1)
References
63(2)
4 SM Identification of State-Space LPV Systems
65(30)
C. Novara
1 Introduction
65(2)
2 Notation and basic notions
67(1)
3 Set membership identification of state-space LPV systems
68(4)
4 Interpolatory and optimal estimates
72(7)
5 Important aspects of the identification process
79(3)
6 Examples
82(8)
7 Conclusion
90(1)
References
91(4)
5 Identification of Input-Output LPV Models
95(38)
V. Laurain
1 Introduction
95(2)
2 Discrete-time LPV polynomials models
97(3)
3 Estimating LPV-ARX models in DT
100(5)
4 Addressing estimation with general noise models
105(11)
5 Direct estimation of continuous-time LPV systems
116(5)
6 Instrumental variable approach in continuous-time
121(7)
7 Conclusion
128(1)
References
129(4)
6 Reducing the Dimensions of Data Matrices in LPV Subspace Identification
133(34)
V. Verdult
M. Verhaegen
1 Introduction
133(3)
2 Data equations
136(3)
3 Basic ideas behind the methods
139(7)
4 Two-block identification method
146(5)
5 Implementation by selection of dominant rows
151(7)
6 Implementation by a kernel method
158(4)
7 Conclusion
162(1)
References
163(4)
7 Subspace Identification of MIMO LPV Systems
167(34)
J. W. van Wingerden
M. Verhaegen
1 Introduction
167(2)
2 Problem formulation and assumptions
169(3)
3 Factorization of the LPV controllability matrix
172(3)
4 LPV predictor-based subspace identification
175(5)
5 Kernel method
180(6)
6 Simulation examples
186(2)
7 Case study: A "smart" airfoil
188(8)
8 Conclusion
196(1)
References
196(5)
8 Subspace Identification of Continuous-Time State-Space LPV Models
201(30)
M. Bergamasco
M. Lovera
1 Introduction
202(3)
2 Definitions
205(2)
3 Problem statement
207(1)
4 A balanced subspace approach to identification for gain scheduling
207(1)
5 Continuous-time predictor-based subspace identification
208(5)
6 Balancing of the identified models
213(2)
7 Model interpolation
215(3)
8 Comments and discussion
218(1)
9 Simulation examples
219(8)
10 Conclusions
227(1)
11 Acknowledgements
227(1)
References
227(4)
9 Indirect Continuous-Time LPV System Identification
231(28)
P. Lopes dos Santos
1 Introduction
232(2)
2 LPV systems
234(1)
3 Discretisation of LPV systems
235(3)
4 Successive approximations identification algorithm
238(2)
5 Downsampled LTI discrete-time deterministic-stochastic subspace identification algorithm
240(7)
6 Case study
247(7)
7 Conclusion
254(1)
References
255(4)
10 LPV System Identification Using Series Expansion Models
259(36)
R. Toth
P. S. C. Heuberger
P. M. J. Van den Hof
1 Introduction
259(1)
2 Perspectives of series-expansion models
260(4)
3 Orthonormal basis function models
264(8)
4 Identification via OBF models
272(7)
5 Identification of a high-performance positioning device
279(13)
6 Conclusion
292(1)
References
292(3)
11 System Identification of Linear Parameter Varying State-Space Models
295(22)
A. Wills
B. Ninness
1 Introduction
295(2)
2 Problem formulation
297(1)
3 Maximum-likelihood estimation
298(2)
4 The expectation-maximisation (EM) algorithm for ML estimation
300(1)
5 EM for LPV models
301(6)
6 Simulation study
307(5)
7 Conclusion
312(1)
References
313(4)
12 PWA Identification of Interconnected Systems with LFR Structure
317(30)
S. Paoletti
A. Garulli
1 Introduction
317(2)
2 PWA-LFR models
319(3)
3 Black-box PWA system identification
322(3)
4 Structured identification of PWA-LFR models
325(9)
5 Applications
334(10)
6 Conclusions
344(1)
References
345(2)
13 Identification and Model (In)validation of Switched ARX Systems
347(34)
C. Feng
1 Introduction
348(3)
2 Preliminaries
351(5)
3 System identification
356(9)
4 Model (in)validation
365(5)
5 Numerical examples and applications
370(7)
6 Concluding remarks
377(1)
References
377(4)
Index 381
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