A new optimization-based approach to experiment design for dynamic MIMO identification

Kurt Erik Häggblom

    Research output: Contribution to journalArticleScientificpeer-review

    9 Citations (Scopus)

    Abstract

    A new optimization-based approach to experiment design for dynamic MIMO identification is presented. Unlike previously proposed methods that consider dynamics and constraints, the proposed method directly addresses the distribution of output data to be generated in the experiment. The problem is formulated as a convex optimization problem with constraints expressed as linear matrix inequalities (LMIs). Three optimization objectives considered are to maximize the smallest singular value and the determinant of the output covariance matrix as well as the determinant of the input covariance matrix. The solution can be implemented in a similar way as previously proposed gain-directional designs based on an estimate of the static gain matrix. The type of perturbation (e.g., RBS, PRBS, multisine signal) can be selected by the user. Various aspects of the method are illustrated by the Wood-Berry distillation column.
    Original languageUndefined/Unknown
    Pages (from-to)7321–7326
    JournalIFAC papers online
    Volume50
    Issue number1
    DOIs
    Publication statusPublished - 2017
    MoE publication typeA1 Journal article-refereed

    Keywords

    • system identification
    • Multivariable systems
    • experiment design
    • Input signals
    • Linear matrix inequalities
    • E-optimality
    • Convex optimization
    • Control-oriented models
    • ill-conditioned systems

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