Cover Image

Expectation constrained stochastic nonlinear model predictive control of a bioreactor

Main Authors: Bradford, Eric, Imsland, Lars
Format: Article Journal
Bahasa: eng
Terbitan: Elsevier , 2017
Subjects:
Randomized MPC
Stochastic programming
Monte Carlo method
Uncertainty
Variance reduction
Online Access: https://zenodo.org/record/1036782
ctrlnum 1036782
fullrecord <?xml version="1.0"?> <dc schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd"><creator>Bradford, Eric</creator><creator>Imsland, Lars</creator><date>2017-10-25</date><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.</description><identifier>https://zenodo.org/record/1036782</identifier><identifier>10.5281/zenodo.1036782</identifier><identifier>oai:zenodo.org:1036782</identifier><language>eng</language><publisher>Elsevier</publisher><relation>info:eu-repo/grantAgreement/EC/H2020/675215/</relation><relation>info:eu-repo/semantics/altIdentifier/doi/10.1016/B978-0-444-63965-3.50272-5</relation><relation>doi:10.5281/zenodo.1036781</relation><rights>info:eu-repo/semantics/openAccess</rights><rights>https://creativecommons.org/licenses/by/4.0/legalcode</rights><source>Computer Aided Chemical Engineering 40 1621-1626</source><subject>Randomized MPC</subject><subject>Stochastic programming</subject><subject>Monte Carlo method</subject><subject>Uncertainty</subject><subject>Variance reduction</subject><title>Expectation constrained stochastic nonlinear model predictive control of a bioreactor</title><type>Journal:Article</type><type>Journal:Article</type><recordID>1036782</recordID></dc>
language eng
format Journal:Article
Journal
Journal:Journal
author Bradford, Eric
Imsland, Lars
title Expectation constrained stochastic nonlinear model predictive control of a bioreactor
publisher Elsevier
publishDate 2017
topic Randomized MPC
Stochastic programming
Monte Carlo method
Uncertainty
Variance reduction
url https://zenodo.org/record/1036782
contents 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.
id IOS16997.1036782
institution ZAIN Publications
institution_id 7213
institution_type library:special
library
library Cognizance Journal of Multidisciplinary Studies
library_id 5267
collection Cognizance Journal of Multidisciplinary Studies
repository_id 16997
subject_area Multidisciplinary
city Stockholm
province INTERNASIONAL
shared_to_ipusnas_str 1
repoId IOS16997
first_indexed 2022-06-06T03:01:47Z
last_indexed 2022-06-06T03:01:47Z
recordtype dc
_version_ 1734896945487413248
score 17.538404

Lihat Juga