Identification of Multivariable Linear Parameter Varying Systems Based on Subspace Techniques

V. Verdult, M.H.G. Verhaegen

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    11 Citations (Scopus)
    95 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 languageUndefined
    Title of host publicationProceedings of the 39th IEEE Conference on Decision and Control
    Place of PublicationSydney, Australia
    PublisherIEEE
    Pages1567-1572
    Number of pages6
    ISBN (Print)9780780366381
    DOIs
    Publication statusPublished - 15 Dec 2000
    Event39th IEEE Conference on Decision and Control, CDC 2000 - Sydney Convention and Exhibition Centre , Sydney, Australia
    Duration: 12 Dec 200015 Dec 2000
    Conference number: 39

    Publication series

    Name
    PublisherIEEE
    Volume2

    Conference

    Conference39th IEEE Conference on Decision and Control, CDC 2000
    Abbreviated titleCDC
    CountryAustralia
    CitySydney
    Period12/12/0015/12/00

    Keywords

    • METIS-130453
    • IR-25651

    Cite this

    Verdult, V., & Verhaegen, M. H. G. (2000). Identification of Multivariable Linear Parameter Varying Systems Based on Subspace Techniques. In Proceedings of the 39th IEEE Conference on Decision and Control (pp. 1567-1572). Sydney, Australia: IEEE. https://doi.org/10.1109/CDC.2000.912083