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On Estimating Initial Conditions in Unstructured Models
KTH, School of Electrical Engineering (EES), Automatic Control.
KTH, School of Electrical Engineering (EES), Automatic Control.ORCID iD: 0000-0003-0355-2663
KTH, School of Electrical Engineering (EES), Automatic Control.ORCID iD: 0000-0002-9368-3079
2015 (English)In: 2015 54th IEEE Conference on Decision and Control (CDC), IEEE conference proceedings, 2015, 2725-2730 p.Conference paper (Refereed)
Abstract [en]

Estimation of structured models is an importantproblem in system identification. Some methods, as an intermediatestep to obtain the model of interest, estimate theimpulse response parameters of the system. This approachdates back to the beginning of subspace identification and isstill used in recently proposed methods. A limitation of thisprocedure is that, when obtaining these parameters from ahigh-order unstructured model, the initial conditions of thesystem are typically unknown, which imposes a truncation ofthe measured output data for the estimation. For finite samplesizes, discarding part of the data limits the performance ofthe method. To deal with this issue, we propose an approachthat uses all the available data, and estimates also the initialconditions of the system. Then, as examples, we show how thisapproach can be applied to two methods in a beneficial manner.Finally, we use a simulation study to exemplify the potential ofthe approach.

Place, publisher, year, edition, pages
IEEE conference proceedings, 2015. 2725-2730 p.
National Category
Control Engineering
URN: urn:nbn:se:kth:diva-179576DOI: 10.1109/CDC.2015.7402628ScopusID: 2-s2.0-84962016460ISBN: 978-1-4799-7884-7OAI: diva2:884954
54th IEEE Conference on Decision and Control, Osaka, Japan, December 15-18, 2015

QC 20160318

Available from: 2015-12-17 Created: 2015-12-17 Last updated: 2016-03-18Bibliographically approved

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