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    Stochastic model predictive control for tracking linear systems

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    pericles_1099151441.txt (1.267Kb)
    Date
    2019-04-11
    Author
    D' Jorge, Agustina
    Santoro, Bruno
    Anderson, Alejandro Luis
    González, Alejandro Hernán
    Ferramosca, Antonio
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    Abstract
    This note presents a stochastic formulation of the model predictive control for tracking (MPCT), based on the results of the work of Lorenzen et al. The proposed controller ensures constraints satisfaction in probability, and maintains the main features of the MPCT, that are feasibility for any changing setpoints and enlarged domain of attraction, even larger than the one delivered by Lorenzen et al, thanks to the use of artificial references and relaxed terminal constraints. The asymptotic stability (in probability) of the minimal robust positively invariant set centered on the desired setpoint is guaranteed. Simulations on a DC-DC converter show the benefits and the properties of the proposal.
     
    CONICET ‐ Universidad Tecnológica Nacional, Facultad Regional de Reconquista, Santa Fe, Argentina
     
    URI
    http://hdl.handle.net/20.500.12272/4923
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