Multivariable uncertainty estimation based on multi-model output matching

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    This paper describes a procedure for deriving norm-bounded output-multiplicative uncertainty descriptions for a multi-input multi-output system by matching the output of an uncertainty model to the outputs of a set of known models. It is assumed that the set of models has been obtained through system identification. The objective is to determine the least conservative uncertainty description such that all known experimental data can be reconstructed by the uncertainty model. Both unstructured and diagonal uncertainty are considered as well as various structures of the uncertainty weight matrix. For the case with no a priori information, it is shown that a nonconservative uncertainty description can be obtained by minimizing the magnitude of the determinant of the uncertainty weight matrix subject to the output-matching condition. The procedure is illustrated by estimation of uncertainty weights and design of mu-optimal controllers for a distillation column. (C) 2003 Elsevier Ltd. All rights reserved.
    Original languageUndefined/Unknown
    Pages (from-to)293–304
    Number of pages12
    JournalJournal of Process Control
    Issue number3
    Publication statusPublished - 2004
    MoE publication typeA1 Journal article-refereed


    • distillation control
    • model validation
    • multiple models
    • robust control
    • uncertainty estimation

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