Sampling Random Transfer Functions

Constantino Manuel Lagoa, X. Li, M. C. Mazzaro, M. Sznaier

    Research output: Contribution to journalConference article

    4 Scopus citations

    Abstract

    Recently, considerable attention has been paid to the use of probabilistic algorithms for analysis and design of robust control systems. However, since these algorithms require the generation of random samples of the uncertain parameters, their application has been mostly limited to the case of parametric uncertainty. Notable exceptions to this limitation are the algorithm for generating FIR transfer functions in Lagoa et al. and the algorithm for generating random fixed order state space representations in Calafiore et al. In this paper, we provide the means for further extending the use of probabilistic algorithms for the case of dynamic causal uncertain parameters. More precisely, we exploit both time and frequency domain characterizations to develop efficient algorithms for generation of random samples of causal, linear time-invariant uncertain transfer functions. The usefulness of these tools will be illustrated by developing an algorithm for solving some multi-disk problems arising in the context of synthesizing robust controllers for systems subject to structured dynamic uncertainty.

    Original languageEnglish (US)
    Pages (from-to)2429-2434
    Number of pages6
    JournalProceedings of the IEEE Conference on Decision and Control
    Volume3
    StatePublished - Dec 1 2003
    Event42nd IEEE Conference on Decision and Control - Maui, HI, United States
    Duration: Dec 9 2003Dec 12 2003

    All Science Journal Classification (ASJC) codes

    • Control and Systems Engineering
    • Modeling and Simulation
    • Control and Optimization

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