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    Event-triggered probabilistic robust control of linear systems with input constrains: By scenario optimization approach

    Access Status
    Open access via publisher
    Authors
    Yin, YanYan
    Liu, Y.
    Teo, Kok Lay
    Wang, S.
    Date
    2017
    Type
    Journal Article
    
    Metadata
    Show full item record
    Citation
    Yin, Y. and Liu, Y. and Teo, K.L. and Wang, S. 2017. Event-triggered probabilistic robust control of linear systems with input constrains: By scenario optimization approach. International Journal of Robust and Nonlinear Control. 28 (1): pp. 144-153.
    Source Title
    International Journal of Robust and Nonlinear Control
    DOI
    10.1002/rnc.3858
    ISSN
    1049-8923
    School
    Department of Mathematics and Statistics
    URI
    http://hdl.handle.net/20.500.11937/54769
    Collection
    • Curtin Research Publications
    Abstract

    This paper addresses the problem of probabilistic robust stabilization for uncertain systems subject to input saturation. A new probabilistic solution framework for robust control analysis and synthesis problems is addressed by a scenario optimization approach, in which the uncertainties are not assumed to be norm bounded. Furthermore, by expressing the saturated linear feedback law on a convex hull of a group of auxiliary linear feedback laws, we establish conditions under which the closed-loop system is probabilistic stable. Based on these conditions, the problem of designing the state feedback gains for achieving the largest size of the domain of attraction is formulated and solved as a constrained optimization problem with linear matrix inequality constraints. The results are then illustrated by a numerical example.

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