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

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    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.

    Original languageUndefined/Unknown
    Title of host publicationThe 11th International Conference on Control, Automation, Robotics and Vision
    ISBN (Electronic)978-1-4244-7815-6
    ISBN (Print)978-1-4244-7814-9
    Publication statusPublished - 2010
    MoE publication typeA4 Article in a conference publication
    Eventconference; 2010-12-07; 2010-12-10 - 11th International Conference on Control, Automation, Robotics and Vision (ICARCV 2010)
    Duration: 7 Dec 201010 Dec 2010


    Conferenceconference; 2010-12-07; 2010-12-10


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

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