Convex formulations for data-based uncertainty minimization of linear uncertainty models

    Tutkimustuotos: Artikkeli kirjassa/raportissa/konferenssijulkaisussaKonferenssiartikkeliTieteellinenvertaisarvioitu

    4 Sitaatiot (Scopus)

    Abstrakti

    Convex formulations are derived for the minimization of uncertainty bounds with respect to a nominal model and given input-output data for general uncertainty models of LFT type. The known data give rise to data-matching conditions that have to be satisfied. It is shown how these conditions, which originally are in the form of BMIs for a number of uncertainty models, can be transformed to LMIs, thus making the optimization problem convex. These formulations make it easy to find the best uncertainty model from a number of alternatives for robust control design.

    AlkuperäiskieliEi tiedossa
    OtsikkoThe 11th International Conference on Control, Automation, Robotics and Vision
    KustantajaIEEE
    Sivut501–505
    ISBN (elektroninen)978-1-4244-7815-6
    ISBN (painettu)978-1-4244-7814-9
    DOI - pysyväislinkit
    TilaJulkaistu - 2010
    OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisuussa
    Tapahtumaconference; 2010-12-07; 2010-12-10 - 11th International Conference on Control, Automation, Robotics and Vision (ICARCV 2010)
    Kesto: 7 joulukuuta 201010 joulukuuta 2010

    Konferenssi

    Konferenssiconference; 2010-12-07; 2010-12-10
    Ajanjakso07/12/1010/12/10

    Keywords

    • Convex optimization
    • Distillation columns
    • LFT uncertainty
    • Linear matrix inequalities
    • Linear multivariable systems
    • Robust control
    • Uncertainty modeling

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