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Asymptotic statistical analysis for model-based control design strategies
The University of Newcastle, Australia.
KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre. (System Identification Group)ORCID iD: 0000-0003-0355-2663
The University of Newcastle, Australia.
2011 (English)In: Automatica, ISSN 0005-1098, E-ISSN 1873-2836, Vol. 47, no 5, 1041-1046 p.Article in journal (Refereed) Published
Abstract [en]

In this paper, we generalize existing fundamental limitations on the accuracy of the estimation of dynamic models. In addition, we study the large sample statistical behavior of different estimation-based controller design strategies. In particular, fundamental limitations on the closed-loop performance using a controller obtained by Virtual Reference Feedback Tuning (VRFT) are studied. We also extend our results to more general estimation-based control design strategies. We present numerical examples to show the application of our results.

Place, publisher, year, edition, pages
2011. Vol. 47, no 5, 1041-1046 p.
Keyword [en]
System identification, Maximum likelihood, Model-based control
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:kth:diva-34243DOI: 10.1016/j.automatica.2011.01.058ISI: 000290502200022Scopus ID: 2-s2.0-79954875924OAI: oai:DiVA.org:kth-34243DiVA: diva2:422624
Note

QC 20150730

Available from: 2011-06-13 Created: 2011-05-30 Last updated: 2017-12-11Bibliographically approved

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Rojas, Cristian R.

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