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Data-Driven Input-Passivity Estimation Using Power Iterations
KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control).ORCID iD: 0000-0002-6322-7857
Univ Stuttgart, Inst Syst Theory & Automat Control, Stuttgart, Germany..
Univ Stuttgart, Inst Syst Theory & Automat Control, Stuttgart, Germany..
KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control).ORCID iD: 0000-0003-0355-2663
2021 (English)In: IFAC PAPERSONLINE, ELSEVIER , 2021, Vol. 54, no 7, p. 619-624Conference paper, Published paper (Refereed)
Abstract [en]

In this work we develop a non-parametric method to estimate the input-passivity index of an unknown linear and time-invariant (LTI) system from iterative experiments based on the power method from numerical linear algebra. Inspired by the power method for estimating the H-infinity-norm (or l(2)-gain) from data, we propose an algorithm that time-reverses input-output data in order to emulate measurements of a virtual system whose l(2)-gain matches the passivity index of the original system under study. While the proposed method requires exciting the original system twice, we also introduce an improved sampling scheme where only one experiment per iteration is needed.

Place, publisher, year, edition, pages
ELSEVIER , 2021. Vol. 54, no 7, p. 619-624
Keywords [en]
Experiment design, nonparametric methods, identification for control data-driven learning, estimation algorithms
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:kth:diva-303778DOI: 10.1016/j.ifacol.2021.08.429ISI: 000696396200102Scopus ID: 2-s2.0-85118185076OAI: oai:DiVA.org:kth-303778DiVA, id: diva2:1605341
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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Müller, Matias I.Rojas, Cristian R.

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