An algorithm for sampling subsets of H∞ with applications to risk-adjusted performance analysis and model (In)validation

Mario Sznaier, Constantino Manuel Lagoa, Maria Cecilia Mazzaro

    Research output: Contribution to journalArticle

    17 Scopus citations

    Abstract

    In spite of their potential to reduce computational complexity, the use of probabilistic methods in robust control has been mostly limited to parametric uncertainty, since the problem of sampling causal bounded operators is largely open. In this note, we take steps toward removing this limitation by proposing a computationally efficient algorithm aimed at uniformly sampling suitably chosen subsets of H∞. As we show in the note, samples taken from these sets can be used to carry out model (in)validation and robust performance analysis in the presence of structured dynamic linear time-invariant uncertainty, problems known to be NP-hard in the number of uncertainty blocks.

    Original languageEnglish (US)
    Pages (from-to)410-416
    Number of pages7
    JournalIEEE Transactions on Automatic Control
    Volume50
    Issue number3
    DOIs
    StatePublished - Mar 1 2005

    All Science Journal Classification (ASJC) codes

    • Control and Systems Engineering
    • Computer Science Applications
    • Electrical and Electronic Engineering

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