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Published October 25, 2017 | Version v1
Journal article Open

Expectation constrained stochastic nonlinear model predictive control of a bioreactor

  • 1. Norwegian University of Science and Technology

Description

Nonlinear model predictive control is a popular control approach for highly nonlinear and unsteady state processes, which however can fail due to unaccounted uncertainties. This paper proposes to apply a sample-average approach to solve the general stochastic nonlinear model predictive control problem to handle probabilistic uncertainties. Each sample represents a nonlinear simulation, which is expensive. Therefore, variance-reduction methods were systematically compared to lower the necessary number of samples. The method was shown to perform well on a semi-batch bioreactor case-study compared to a nominal nonlinear model predictive controller. Expectation constraints were employed to deal with state constraints in this case-study, which take into account both magnitude and probability of deviations.

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Funding

PRONTO – PRONTO: PROcess NeTwork Optimization for efficient and sustainable operation of Europe’s process industries taking machinery condition and process performance into account. 675215
European Commission