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A Geometric Approach to Variance Analysis of Cascaded Systems
KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre. (System Identification)ORCID iD: 0000-0002-1127-1397
KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre. (System Identification)ORCID iD: 0000-0003-0355-2663
KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre. (System Identification)ORCID iD: 0000-0002-9368-3079
2013 (English)In: Proceedings of the 52nd Conference On Decision And Control, IEEE conference proceedings, 2013, 6496-6501 p.Conference paper, Published paper (Refereed)
Abstract [en]

Modeling complex and interconnected systems is a key issue in system identification. When estimating individual subsystems of a network of interconnected system, it is of interest to know the improvement of model-accuracy in using different sensors and actuators. In this paper, using a geometric approach, we quantify the accuracy improvement from additional sensors when estimating the first of a set of subsystems connected in a cascade structure. We present results on how the zeros of the first subsystem affect the accuracy of the corresponding model. Additionally we shed some light on how structural properties and experimental conditions determine the accuracy. The results are particularized to FIR systems, for which the results are illustrated by numerical simulations. A surprising special case occurs when the first subsystem contains a zero on the unit circle; as the model orders grows large, thevariance of the frequency function estimate, evaluated at thecorresponding frequency of the unit-circle zero, is shown to be the same as if the other subsystems were completely known.

Place, publisher, year, edition, pages
IEEE conference proceedings, 2013. 6496-6501 p.
Keyword [en]
System Identification, Asymptotic covariance, Cascaded systems, Structured identification, Identification of dynamic networks
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:kth:diva-138167ISI: 000352223507054Scopus ID: 2-s2.0-84902308632ISBN: 978-146735717-3 (print)OAI: oai:DiVA.org:kth-138167DiVA: diva2:680684
Conference
52nd IEEE Conference on Decision and Control, CDC 2013; Florence; Italy; 10 December 2013 through 13 December 2013
Funder
Swedish Research Council, 621-2009-4017EU, European Research Council, 267381
Note

QC 20150703

Available from: 2013-12-18 Created: 2013-12-18 Last updated: 2015-12-08Bibliographically approved

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Everitt, NiklasRojas, Cristian R.Hjalmarsson, Håkan

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