Improved design of experiments for identification of MIMO systems

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


    A problem in the identification of MIMO systems is that the system outputs in an identification experiment may be strongly correlated if the inputs are perturbed by uncorrelated signals, as is standard practice. Such a correlation reduces identifiability.A set of methods to design input perturbations that minimize the sample correlation between the outputs has previously been proposed. These methods require an initial model for the design. In this paper, a data-based design method is proposed. Data are obtained from preliminary experiments with the system to be identified. Besides being preferable from a practical point of view, the data-based approach makes it easier to handle some numerical issues that gave problems in the model-based approach.A design method that minimizes the input or output peak value subject to desired output variances with no output correlation is presented. A model of an ill-conditioned distillation column is used to illustrate the method.

    Original languageUndefined/Unknown
    Title of host publication29th European symposium on computer aided process engineering
    EditorsAnton A. Kiss, Edwin Zondervan, Richard Lakerveld, Leyla Özkan
    ISBN (Electronic)9780128186350
    ISBN (Print)978-0-12-818634-3
    Publication statusPublished - 2019
    MoE publication typeA4 Article in a conference publication
    EventEuropean Symposium on Computer Aided Chemical Engineering - 29th European symposium on computer aided process engineering (ESCAPE-29)
    Duration: 16 Jun 201919 Jun 2019


    ConferenceEuropean Symposium on Computer Aided Chemical Engineering


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

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