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

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

6 Citations (Scopus)

1 Downloads (Pure)

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.

Original language	English
Title of host publication	2017 IEEE 56th Annual Conference on Decision and Control, CDC 2017
Place of Publication	Piscataway
Publisher	Institute of Electrical and Electronics Engineers
Pages	1560-1565
Number of pages	6
ISBN (Electronic)	978-1-5090-2873-3
DOIs	https://doi.org/10.1109/CDC.2017.8263873
Publication status	Published - 18 Jan 2018
Event	56th IEEE Conference on Decision and Control (CDC 2017) - Melbourne, VIC, Australia, Melbourne, Australia Duration: 12 Dec 2017 → 15 Dec 2017 Conference number: 56 http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=8253407

Conference

Conference	56th IEEE Conference on Decision and Control (CDC 2017)
Abbreviated title	CDC 2017
Country/Territory	Australia
City	Melbourne
Period	12/12/17 → 15/12/17
Internet address	http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=8253407

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10.1109/CDC.2017.8263873

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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.",

author = "G. Rallo and S. Formentin and C.R. Rojas and T.A.E. Oomen and S.M. Savaresi",

year = "2018",

month = jan,

day = "18",

doi = "10.1109/CDC.2017.8263873",

language = "English",

pages = "1560--1565",

booktitle = "2017 IEEE 56th Annual Conference on Decision and Control, CDC 2017",

publisher = "Institute of Electrical and Electronics Engineers",

address = "United States",

note = "56th IEEE Conference on Decision and Control (CDC 2017), CDC 2017 ; Conference date: 12-12-2017 Through 15-12-2017",

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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, pp. 1560-1565, 56th IEEE Conference on Decision and Control (CDC 2017), Melbourne, Australia, 12/12/17. https://doi.org/10.1109/CDC.2017.8263873

TY - GEN

T1 - Data-driven H∞-norm estimation via expert advice

AU - Rallo, G.

AU - Formentin, S.

AU - Rojas, C.R.

AU - Oomen, T.A.E.

AU - Savaresi, S.M.

N1 - Conference code: 56

PY - 2018/1/18

Y1 - 2018/1/18

N2 - 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.

AB - 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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U2 - 10.1109/CDC.2017.8263873

DO - 10.1109/CDC.2017.8263873

M3 - Conference contribution

AN - SCOPUS:85046129816

SP - 1560

EP - 1565

BT - 2017 IEEE 56th Annual Conference on Decision and Control, CDC 2017

PB - Institute of Electrical and Electronics Engineers

CY - Piscataway

T2 - 56th IEEE Conference on Decision and Control (CDC 2017)

Y2 - 12 December 2017 through 15 December 2017

ER -

Data-driven H_∞-norm estimation via expert advice

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