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

    Forskningsoutput: Kapitel i bok/konferenshandlingKonferensbidragVetenskapligPeer review

    4 Citeringar (Scopus)


    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.

    Titel på gästpublikationThe 11th International Conference on Control, Automation, Robotics and Vision
    ISBN (elektroniskt)978-1-4244-7815-6
    ISBN (tryckt)978-1-4244-7814-9
    StatusPublicerad - 2010
    MoE-publikationstypA4 Artikel i en konferenspublikation
    Evenemangconference; 2010-12-07; 2010-12-10 - 11th International Conference on Control, Automation, Robotics and Vision (ICARCV 2010)
    Varaktighet: 7 dec 201010 dec 2010


    Konferensconference; 2010-12-07; 2010-12-10


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

    Citera det här