dc.contributor.author | Bradford, Eric Christopher | |
dc.contributor.author | Imsland, Lars Struen | |
dc.date.accessioned | 2018-03-20T11:58:06Z | |
dc.date.available | 2018-03-20T11:58:06Z | |
dc.date.created | 2018-01-14T10:22:21Z | |
dc.date.issued | 2017 | |
dc.identifier.isbn | 978-0-444-63965-3 | |
dc.identifier.uri | http://hdl.handle.net/11250/2491242 | |
dc.description.abstract | 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. | nb_NO |
dc.language.iso | eng | nb_NO |
dc.publisher | Elsevier | nb_NO |
dc.relation.ispartof | 27 European Symposium on Computer Aided Process Engineering | |
dc.title | Expectation constrained stochastic nonlinear model predictive control of a batch bioreactor | nb_NO |
dc.type | Chapter | nb_NO |
dc.description.version | acceptedVersion | nb_NO |
dc.identifier.doi | 10.1016/B978-0-444-63965-3.50272-5 | |
dc.identifier.cristin | 1542148 | |
dc.relation.project | EC/H2020/675215 | nb_NO |
dc.description.localcode | This chapter will not be available due to copyright restrictions (c) 2017 by Elsevier | nb_NO |
cristin.unitcode | 194,63,25,0 | |
cristin.unitname | Institutt for teknisk kybernetikk | |
cristin.ispublished | true | |
cristin.fulltext | preprint | |
cristin.qualitycode | 1 | |