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Data Informativity for the Closed-Loop Identification of MISO ARX Systems
KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control).
Univ Lyon, Lab Ampere, Ecole Cent Lyon, UMR CNRS 5005, Lyon, France..
Univ Lyon, Lab Ampere, Ecole Cent Lyon, UMR CNRS 5005, Lyon, France.;Ctr Natl Rech Sci CNRS, Paris, France..
2021 (English)In: IFAC PAPERSONLINE, Elsevier BV , 2021, Vol. 54, no 7, p. 779-784Conference paper, Published paper (Refereed)
Abstract [en]

In the Prediction Error identification framework, it is crucial that the collected data are informative with respect to the chosen model structure to get a consistent estimate. In this work, we focus on the data informativity property for the identification of multi-inputs single-output ARX systems in closed-loop and we derive a necessary and sufficient condition to verify if a given multisine external excitation combined with the feedback introduced by the controller yields informative data with respect to the chosen model structure.

Place, publisher, year, edition, pages
Elsevier BV , 2021. Vol. 54, no 7, p. 779-784
Keywords [en]
System Identification, Data Informativity, Prediction Error Method, Consistency
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:kth:diva-303771DOI: 10.1016/j.ifacol.2021.08.456ISI: 000696396200129Scopus ID: 2-s2.0-85118188327OAI: oai:DiVA.org:kth-303771DiVA, id: diva2:1605342
Conference
19th IFAC Symposium on System Identification (SYSID), JUL 13-16, 2021, Padova, ITALY
Note

QC 20211022

Available from: 2021-10-22 Created: 2021-10-22 Last updated: 2022-06-25Bibliographically approved

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Colin, Kevin

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  • ieee
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  • de-DE
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  • Other locale
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Output format
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  • asciidoc
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