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    Uncertainty characterization in robust MPC using an approximate convex hull

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    http://hdl.handle.net/11124/170672
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    Sartipizadeh_mines_0052E_11192.pdf (1.746Mb)
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    Abstract
    Model Predictive Control (MPC) of processes when uncertainty is involved is the topic of this thesis. Specifically, a method to characterize parametric uncertainty for robust model predictive control is studied. The goal is to reduce the computational complexity of robust MPC and robust Moving Horizon Estimation (MHE). The main element of this method is the computation of an approximate convex hull that approximately covers the system uncertainty in a new output prediction mapping. Given the complete uncertainty set, an approximate convex hull is computed to determine an efficient set of extreme ...
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    Author(s)
    Sartipizadeh, Hossein

    Advisor(s)
    Vincent, Tyrone; Kee, R. J.

    Date Issued
    2017
    Format
    born digital; doctoral dissertations
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    • 2017 - Mines Theses & Dissertations

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