Numerically reliable identification of fast sampled systems: a novel δ-domain data-dependent orthonormal polynomial approach

Robbert Voorhoeve; Tom Oomen

doi:10.1109/CDC.2018.8619243

Numerically reliable identification of fast sampled systems: a novel δ-domain data-dependent orthonormal polynomial approach

Robbert Voorhoeve
, Tom Oomen

Control Systems Technology

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

1 Citation (Scopus)

Abstract

The practical utility of system identification algorithms is often limited by the reliability of their implementation in finite precision arithmetic. The aim of this paper is to develop a method for the numerically reliable identification of fast sampled systems. In this paper, a data-dependent orthonormal polynomial approach is developed for systems parametrized in the δ -domain. This effectively addresses both the numerical conditioning issues encountered in frequency-domain system identification and the inherent numerical round-off problems of fast-sampled systems in the common Z-domain description. Superiority of the proposed approach is shown in an example.

Original language	English
Title of host publication	2018 IEEE Conference on Decision and Control, CDC 2018
Place of Publication	Piscataway
Publisher	Institute of Electrical and Electronics Engineers
Pages	1433-1438
Number of pages	6
ISBN (Electronic)	978-1-5386-1395-5
ISBN (Print)	978-1-5386-1396-2
DOIs	https://doi.org/10.1109/CDC.2018.8619243
Publication status	Published - 18 Jan 2019
Event	57th IEEE Conference on Decision and Control, CDC 2018 - Miami, United States Duration: 17 Dec 2018 → 19 Dec 2018 Conference number: 57

Conference

Conference	57th IEEE Conference on Decision and Control, CDC 2018
Abbreviated title	CDC 2018
Country/Territory	United States
City	Miami
Period	17/12/18 → 19/12/18

Funding

This research is supported by ASML Research and is part of the research programme VIDI with project number 15698, financed by the Netherlands Organization for Scientific Research (NWO).

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10.1109/CDC.2018.8619243

Cite this

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title = "Numerically reliable identification of fast sampled systems: a novel δ-domain data-dependent orthonormal polynomial approach",

abstract = "The practical utility of system identification algorithms is often limited by the reliability of their implementation in finite precision arithmetic. The aim of this paper is to develop a method for the numerically reliable identification of fast sampled systems. In this paper, a data-dependent orthonormal polynomial approach is developed for systems parametrized in the δ -domain. This effectively addresses both the numerical conditioning issues encountered in frequency-domain system identification and the inherent numerical round-off problems of fast-sampled systems in the common Z-domain description. Superiority of the proposed approach is shown in an example.",

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booktitle = "2018 IEEE Conference on Decision and Control, CDC 2018",

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address = "United States",

note = "57th IEEE Conference on Decision and Control, CDC 2018, CDC 2018 ; Conference date: 17-12-2018 Through 19-12-2018",

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Voorhoeve, R & Oomen, T 2019, Numerically reliable identification of fast sampled systems: a novel δ-domain data-dependent orthonormal polynomial approach. in 2018 IEEE Conference on Decision and Control, CDC 2018., 8619243, Institute of Electrical and Electronics Engineers, Piscataway, pp. 1433-1438, 57th IEEE Conference on Decision and Control, CDC 2018, Miami, Florida, United States, 17/12/18. https://doi.org/10.1109/CDC.2018.8619243

Numerically reliable identification of fast sampled systems: a novel δ-domain data-dependent orthonormal polynomial approach. / Voorhoeve, Robbert; Oomen, Tom.
2018 IEEE Conference on Decision and Control, CDC 2018. Piscataway: Institute of Electrical and Electronics Engineers, 2019. p. 1433-1438 8619243.

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

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N2 - The practical utility of system identification algorithms is often limited by the reliability of their implementation in finite precision arithmetic. The aim of this paper is to develop a method for the numerically reliable identification of fast sampled systems. In this paper, a data-dependent orthonormal polynomial approach is developed for systems parametrized in the δ -domain. This effectively addresses both the numerical conditioning issues encountered in frequency-domain system identification and the inherent numerical round-off problems of fast-sampled systems in the common Z-domain description. Superiority of the proposed approach is shown in an example.

AB - The practical utility of system identification algorithms is often limited by the reliability of their implementation in finite precision arithmetic. The aim of this paper is to develop a method for the numerically reliable identification of fast sampled systems. In this paper, a data-dependent orthonormal polynomial approach is developed for systems parametrized in the δ -domain. This effectively addresses both the numerical conditioning issues encountered in frequency-domain system identification and the inherent numerical round-off problems of fast-sampled systems in the common Z-domain description. Superiority of the proposed approach is shown in an example.

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BT - 2018 IEEE Conference on Decision and Control, CDC 2018

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ER -

Numerically reliable identification of fast sampled systems: a novel δ-domain data-dependent orthonormal polynomial approach

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