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On optimal input design in system identification for control
KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre. (System Identification Group)ORCID iD: 0000-0002-1927-1690
KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.ORCID iD: 0000-0002-9368-3079
KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre. KTH, School of Electrical Engineering (EES), Automatic Control. (System Identification Group)
2010 (English)In: 49TH IEEE CONFERENCE ON DECISION AND CONTROL (CDC), IEEE , 2010, 5548-5553 p.Conference paper (Refereed)
Abstract [en]

This paper considers a recently proposed framework for experiment design in system identification for control. We study model based control design methods, such as Model Predictive Control, where the model is obtained by means of a prediction error system identification method. The degradation in control performance due to uncertainty in the model estimate is specified by an application cost function. The objective is to find a minimum variance input signal, to be used in system identification experiment, such that the control application specification is guaranteed with a given probability when using the estimated model in the control design. We provide insight in the potentials of this approach by finite impulse response model examples, for which it is possible to analytically solve the optimal input problem. The examples show how the control specifications directly affect the excitation conditions in the system identification experiment.

Place, publisher, year, edition, pages
IEEE , 2010. 5548-5553 p.
Keyword [en]
Application cost, Control applications, Control design, Control specifications, Estimated model, Excitation conditions, Experiment design, Finite impulse response model, In-control, Input problem, Input signal, Minimum variance, Model based control design, Model estimates, Optimal input design, Prediction errors, System identifications, Design, Experiments, Identification (control systems), Impulse response, Optimization, Predictive control systems, Specifications, Uncertainty analysis, Model predictive control
National Category
Control Engineering
URN: urn:nbn:se:kth:diva-55386DOI: 10.1109/CDC.2010.5717863ISI: 000295049106051ScopusID: 2-s2.0-79953133266ISBN: 978-1-4244-7746-3OAI: diva2:471655
49th IEEE Conference on Decision and Control (CDC). Atlanta, GA. DEC 15-17, 2010
© 2010 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. QC 20120104Available from: 2012-01-31 Created: 2012-01-02 Last updated: 2013-09-05Bibliographically approved

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Wahlberg, BoHjalmarsson, HåkanAnnergren, Mariette
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