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    Predictive control of convex polyhedron LPV systems with Markov jumping parameters

    Access Status
    Fulltext not available
    Authors
    Yin, YanYan
    Liu, F.
    Shi, P.
    Karimi, H.
    Date
    2012
    Type
    Conference Paper
    
    Metadata
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    Citation
    Yin, Y. and Liu, F. and Shi, P. and Karimi, H. 2012. Predictive control of convex polyhedron LPV systems with Markov jumping parameters, in na (ed), 2012 24th Chinese Control and Decision Conference (CCDC 2012), May 25 2012, pp. 603-608. Taiyuan, China: IEEE.
    Source Title
    Proceedings of the 2012 24th Chinese Control and Decision Conference, CCDC 2012
    Source Conference
    2012 24th Chinese Control and Decision Conference (CCDC 2012)
    DOI
    10.1109/CCDC.2012.6244093
    ISBN
    9781457720727
    URI
    http://hdl.handle.net/20.500.11937/66246
    Collection
    • Curtin Research Publications
    Abstract

    The problem of receding horizon predictive control of stochastic linear parameter varying systems is discussed. First, constant coefficient matrices are obtained at each vertex in the interior of linear parameter varying system, and then, by considering semi-definite programming constraints, weight coefficients between each vertex are calculated, and the equal coefficients matrices for the time variable system are obtained. Second, in the given receding horizon, for each mode sequence of the stochastic convex polyhedron linear parameter varying systems, the optimal control input sequences are designed in order to make the states into a terminal invariant set. Outside of the receding horizon, stability of the system is guaranteed by searching a state feedback control law. Finally, receding horizon predictive controller is designed in terms of linear matrix inequality for such system. Simulation example shows the validity of this method. © 2012 IEEE.

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