Data-driven H∞-norm estimation via expert advice

G. Rallo; S. Formentin; C.R. Rojas; T.A.E. Oomen; S.M. Savaresi

doi:10.1109/CDC.2017.8263873

Data-driven H_∞-norm estimation via expert advice

G. Rallo, S. Formentin, C.R. Rojas, T.A.E. Oomen, S.M. Savaresi

Control Systems Technology

Onderzoeksoutput: Hoofdstuk in Boek/Rapport/Congresprocedure › Conferentiebijdrage › Academic › peer review

10 Citaten (Scopus)

1 Downloads (Pure)

Samenvatting

H_∞-norm estimation is usually an important aspect of robust control design. The aim of this paper is to develop a data-driven estimation method exploiting iterative input design, without requiring parametric modeling. More specifically, the estimation problem is formulated as a sequential game, whose solution is derived within the prediction with expert advice framework. The proposed method is shown to be competitive with the state-of-the-art techniques.

Originele taal-2	Engels
Titel	2017 IEEE 56th Annual Conference on Decision and Control, CDC 2017
Plaats van productie	Piscataway
Uitgeverij	Institute of Electrical and Electronics Engineers
Pagina's	1560-1565
Aantal pagina's	6
ISBN van elektronische versie	978-1-5090-2873-3
DOI's	https://doi.org/10.1109/CDC.2017.8263873
Status	Gepubliceerd - 18 jan. 2018
Evenement	56th IEEE Conference on Decision and Control (CDC 2017), 12-15 December 2017, Melbourne, Australia - Melbourne, VIC, Australia, Melbourne, Australië Duur: 12 dec. 2017 → 15 dec. 2017 Congresnummer: 56 http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=8253407

Congres

Congres	56th IEEE Conference on Decision and Control (CDC 2017), 12-15 December 2017, Melbourne, Australia
Verkorte titel	CDC 2017
Land/Regio	Australië
Stad	Melbourne
Periode	12/12/17 → 15/12/17
Internet adres	http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=8253407

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10.1109/CDC.2017.8263873

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Citeer dit

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title = "Data-driven H∞-norm estimation via expert advice",

abstract = "H∞-norm estimation is usually an important aspect of robust control design. The aim of this paper is to develop a data-driven estimation method exploiting iterative input design, without requiring parametric modeling. More specifically, the estimation problem is formulated as a sequential game, whose solution is derived within the prediction with expert advice framework. The proposed method is shown to be competitive with the state-of-the-art techniques.",

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Rallo, G, Formentin, S, Rojas, CR, Oomen, TAE & Savaresi, SM 2018, Data-driven H_∞-norm estimation via expert advice. in 2017 IEEE 56th Annual Conference on Decision and Control, CDC 2017. Institute of Electrical and Electronics Engineers, Piscataway, blz. 1560-1565, 56th IEEE Conference on Decision and Control (CDC 2017), 12-15 December 2017, Melbourne, Australia, Melbourne, Australië, 12/12/17. https://doi.org/10.1109/CDC.2017.8263873

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