Identification of Multivariable Linear Parameter Varying Systems Based on Subspace Techniques

V. Verdult; M.H.G. Verhaegen

doi:10.1109/CDC.2000.912083

Identification of Multivariable Linear Parameter Varying Systems Based on Subspace Techniques

V. Verdult, M.H.G. Verhaegen

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

13 Citations (Scopus)

257 Downloads (Pure)

Abstract

Presents a subspace type of identification method for multivariable linear parameter-varying systems in state space representation with affine parameter dependence. It is shown that a major problem with subspace methods for this kind of systems is the enormous dimensions of the data matrices involved. To overcome the curse of dimensionality, we suggest to use only the most dominant rows of the data matrices in estimating the model. An efficient selection algorithm is discussed that does not require the formation of the complete data matrices, but can process them row by row

Original language	Undefined
Title of host publication	Proceedings of the 39th IEEE Conference on Decision and Control
Place of Publication	Sydney, Australia
Publisher	IEEE
Pages	1567-1572
Number of pages	6
ISBN (Print)	9780780366381
DOIs	https://doi.org/10.1109/CDC.2000.912083
Publication status	Published - 15 Dec 2000
Event	39th IEEE Conference on Decision and Control, CDC 2000 - Sydney Convention and Exhibition Centre , Sydney, Australia Duration: 12 Dec 2000 → 15 Dec 2000 Conference number: 39

Publication series

Name
Publisher	IEEE
Volume	2

Conference

Conference	39th IEEE Conference on Decision and Control, CDC 2000
Abbreviated title	CDC
Country/Territory	Australia
City	Sydney
Period	12/12/00 → 15/12/00

Keywords

METIS-130453
IR-25651

Access to Document

10.1109/CDC.2000.912083

00912083

Cite this

@inproceedings{c32f45f91f2948f99301b2eab200f8ae,

title = "Identification of Multivariable Linear Parameter Varying Systems Based on Subspace Techniques",

abstract = "Presents a subspace type of identification method for multivariable linear parameter-varying systems in state space representation with affine parameter dependence. It is shown that a major problem with subspace methods for this kind of systems is the enormous dimensions of the data matrices involved. To overcome the curse of dimensionality, we suggest to use only the most dominant rows of the data matrices in estimating the model. An efficient selection algorithm is discussed that does not require the formation of the complete data matrices, but can process them row by row",

keywords = "METIS-130453, IR-25651",

author = "V. Verdult and M.H.G. Verhaegen",

year = "2000",

month = dec,

day = "15",

doi = "10.1109/CDC.2000.912083",

language = "Undefined",

isbn = "9780780366381",

publisher = "IEEE",

pages = "1567--1572",

booktitle = "Proceedings of the 39th IEEE Conference on Decision and Control",

address = "United States",

note = "39th IEEE Conference on Decision and Control, CDC 2000 ; Conference date: 12-12-2000 Through 15-12-2000",

}

Verdult, V & Verhaegen, MHG 2000, Identification of Multivariable Linear Parameter Varying Systems Based on Subspace Techniques. in Proceedings of the 39th IEEE Conference on Decision and Control. IEEE, Sydney, Australia, pp. 1567-1572, 39th IEEE Conference on Decision and Control, CDC 2000, Sydney, New South Wales, Australia, 12/12/00. https://doi.org/10.1109/CDC.2000.912083

TY - GEN

T1 - Identification of Multivariable Linear Parameter Varying Systems Based on Subspace Techniques

AU - Verdult, V.

AU - Verhaegen, M.H.G.

N1 - Conference code: 39

PY - 2000/12/15

Y1 - 2000/12/15

N2 - Presents a subspace type of identification method for multivariable linear parameter-varying systems in state space representation with affine parameter dependence. It is shown that a major problem with subspace methods for this kind of systems is the enormous dimensions of the data matrices involved. To overcome the curse of dimensionality, we suggest to use only the most dominant rows of the data matrices in estimating the model. An efficient selection algorithm is discussed that does not require the formation of the complete data matrices, but can process them row by row

AB - Presents a subspace type of identification method for multivariable linear parameter-varying systems in state space representation with affine parameter dependence. It is shown that a major problem with subspace methods for this kind of systems is the enormous dimensions of the data matrices involved. To overcome the curse of dimensionality, we suggest to use only the most dominant rows of the data matrices in estimating the model. An efficient selection algorithm is discussed that does not require the formation of the complete data matrices, but can process them row by row

KW - METIS-130453

KW - IR-25651

U2 - 10.1109/CDC.2000.912083

DO - 10.1109/CDC.2000.912083

M3 - Conference contribution

SN - 9780780366381

SP - 1567

EP - 1572

BT - Proceedings of the 39th IEEE Conference on Decision and Control

PB - IEEE

CY - Sydney, Australia

T2 - 39th IEEE Conference on Decision and Control, CDC 2000

Y2 - 12 December 2000 through 15 December 2000

ER -