Expectation constrained stochastic nonlinear model predictive control of a batch bioreactor
Chapter
Accepted version
Permanent lenke
http://hdl.handle.net/11250/2491242Utgivelsesdato
2017Metadata
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Originalversjon
10.1016/B978-0-444-63965-3.50272-5Sammendrag
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 non-linear 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.