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Stochastic nonlinear model predictive control of a batch fermentation process

Bradford, Eric; Imsland, Lars Struen
Journal article, Peer reviewed
Published version
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Bradford (Locked)
URI
http://hdl.handle.net/11250/2636935
Date
2019
Metadata
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  • Institutt for teknisk kybernetikk [2238]
  • Publikasjoner fra CRIStin - NTNU [20804]
Original version
Computer-aided chemical engineering. 2019, 46 1237-1242.   10.1016/B978-0-12-818634-3.50207-1
Abstract
Nonlinear model predictive control (NMPC) is an attractive control approach to regulate batch processes reliant on an accurate dynamic model. Most dynamic models however are affected by significant uncertainties, which may lead to worse control performance and infeasibilities, considering the tendency of NMPC to drive the system to its constraints. This paper proposes a novel NMPC framework to mitigate this issue by explicitly taking into account time-invariant stochastic uncertainties. Parametric uncertainties are assumed to be given by so-called polynomial chaos expansions (PCE), which constitutes a flexible approach to depict arbitrary probability distributions. It is assumed that at each sampling time only noisy output measurements are available. The proposed procedure uses a sparse Gauss-Hermite sampling rule to formulate an efficient scenario-based NMPC algorithm based on the PCE, while a stochastic nonlinear filter is employed to update the PCE given the available measurements. The framework is shown to be effective on a challenging semi-batch fermentation process simulation case study.
Publisher
Elsevier
Journal
Computer-aided chemical engineering

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