Experiment designs to minimize input peak and crest factor in MIMO system identification

    Research output: Chapter in Book/Conference proceedingConference contributionScientificpeer-review

    2 Citations (Scopus)

    Abstract

    The quality of the data for system identification is of utmost of importance. Ideally, the output data should satisfy requirements regarding variance, and in the case of multiple-input multiple-output (MIMO) systems, correlation between the outputs. Usually, it is desired to achieve this by input perturbations with peak values as small as possible. A related measure is the crest factor, which is a measure of the (inverse) power of an input perturbation with given peak value. In this paper, the effect of minimizing the input peak and the crest factor, subject to desired output variances/covariances, is studied for various types of perturbation signals. The design procedure is completely data based; data are obtained by one or more preliminary experiments with the system to be identified. A model of an ill-conditioned distillation column is used for illustration.

    Original languageUndefined/Unknown
    Title of host publicationIEEE CCTA 2019 : 3rd IEEE conference on control technology and applications : August 19-21, 2019, City University of Hong Kong, Hong Kong, China
    PublisherIEEE
    Pages499–504
    ISBN (Print)978-1-7281-2767-5
    DOIs
    Publication statusPublished - 2019
    MoE publication typeA4 Article in a conference publication
    EventIEEE Conference on Control Technology and Applications - 3rd IEEE Conference on Control Technology and Applications (IEEE CCTA 2019)
    Duration: 19 Aug 201921 Aug 2019

    Conference

    ConferenceIEEE Conference on Control Technology and Applications
    Period19/08/1921/08/19

    Keywords

    • data-based design
    • experiment design
    • ill-conditioned systems
    • multivariable systems
    • system identification

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