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Application Set Approximation in Optimal Input Design for Model Predictive Control
KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.ORCID iD: 0000-0003-0355-2663
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2014 (English)In: 2014 European Control Conference (ECC), 2014, 744-749 p.Conference paper, Published paper (Refereed)
Abstract [en]

This contribution considers one central aspect of experiment design in system identification, namely application set approximation. When a control design is based on an estimated model, the achievable performance is related to the quality of the estimate. The degradation in control performance due to plant-modeling missmatch is quantified by an application cost function. A convex approximation of the set of models that satisfy the control specification is typically required in optimal input design. The standard approach is to use a quadratic approximation of the application cost function, where the main computational effort is to find the corresponding Hessian matrix. Our main contribution is an alternative approach for this problem, which uses the structure of the underlying optimal control problem to considerably reduce the computations needed to find the application set. This technique allows the use of applications oriented input design for MPC on much more complex plants. The approach is numerically evaluated on a distillation control problem.

Place, publisher, year, edition, pages
2014. 744-749 p.
Keyword [en]
Cost functions, Design, Distillation, Model predictive control, Optimal control systems, Predictive control systems, Achievable performance, Computational effort, Control specifications, Convex approximation, Distillation control, Optimal control problem, Optimal input design, Quadratic approximation
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:kth:diva-137203DOI: 10.1109/ECC.2014.6862496ISI: 000349955701007Scopus ID: 2-s2.0-84911468844ISBN: 978-3-9524269-1-3 (print)OAI: oai:DiVA.org:kth-137203DiVA: diva2:678272
Conference
13th European Control Conference, ECC 2014, Strasbourg Convention and Exhibition Center Place de Bordeaux Strasbourg, France, 24 June 2014 through 27 June 2014
Note

QC 20140113

Available from: 2013-12-11 Created: 2013-12-11 Last updated: 2015-04-09Bibliographically approved

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Rojas, Cristian R.Wahlberg, BoHjalmarsson, Håkan

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Ebadat, AfroozAnnergren, MarietteLarsson, Christian A.Rojas, Cristian R.Wahlberg, BoHjalmarsson, Håkan
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