Parametric Identification using Kernel-based Frequency Response Model with Model Order Selection based on Robust Stability

Hanul Jung; Taejune Kong; Jae Gu Kang; Sehoon Oh

doi:10.1109/IECON49645.2022.9968765

Parametric Identification using Kernel-based Frequency Response Model with Model Order Selection based on Robust Stability

Hanul Jung
, Taejune Kong
, Jae Gu Kang
, Sehoon Oh

Department of Robotics and Mechatronics Engineering

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

1 Scopus citations

Abstract

In this paper, the parametric identification is addressed by a kernel-based model with covariance and a novel model order selection algorithm. The kernel-based model is uti-lized for training the sampled frequency response characteristics, which is insufficient for parametric identification because of noisy and discrete data. The kernel-based frequency response model improves the parametric identification by using the high covariance data. In addition, prior knowledge of the model order is essential for parametric identification. This paper proposes a novel model order selection based on the robust stability criterion of disturbance observer (DOB). The effectiveness of the proposed algorithm is verified through numerical simulations under several conditions.

Original language	English
Title of host publication	IECON 2022 - 48th Annual Conference of the IEEE Industrial Electronics Society
Publisher	IEEE Computer Society
ISBN (Electronic)	9781665480253
DOIs	https://doi.org/10.1109/IECON49645.2022.9968765
State	Published - 2022
Event	48th Annual Conference of the IEEE Industrial Electronics Society, IECON 2022 - Brussels, Belgium Duration: 17 Oct 2022 → 20 Oct 2022

Publication series

Name	IECON Proceedings (Industrial Electronics Conference)
Volume	2022-October
ISSN (Print)	2162-4704
ISSN (Electronic)	2577-1647

Conference

Conference	48th Annual Conference of the IEEE Industrial Electronics Society, IECON 2022
Country/Territory	Belgium
City	Brussels
Period	17/10/22 → 20/10/22

Bibliographical note

Publisher Copyright:
© 2022 IEEE.

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10.1109/IECON49645.2022.9968765

Cite this

Jung, H., Kong, T., Kang, J. G., & Oh, S. (2022). Parametric Identification using Kernel-based Frequency Response Model with Model Order Selection based on Robust Stability. In IECON 2022 - 48th Annual Conference of the IEEE Industrial Electronics Society (IECON Proceedings (Industrial Electronics Conference); Vol. 2022-October). IEEE Computer Society. https://doi.org/10.1109/IECON49645.2022.9968765

@inproceedings{b9339570cd744ea59f8154fadf98f8f2,

title = "Parametric Identification using Kernel-based Frequency Response Model with Model Order Selection based on Robust Stability",

abstract = "In this paper, the parametric identification is addressed by a kernel-based model with covariance and a novel model order selection algorithm. The kernel-based model is uti-lized for training the sampled frequency response characteristics, which is insufficient for parametric identification because of noisy and discrete data. The kernel-based frequency response model improves the parametric identification by using the high covariance data. In addition, prior knowledge of the model order is essential for parametric identification. This paper proposes a novel model order selection based on the robust stability criterion of disturbance observer (DOB). The effectiveness of the proposed algorithm is verified through numerical simulations under several conditions.",

author = "Hanul Jung and Taejune Kong and Kang, \{Jae Gu\} and Sehoon Oh",

note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 48th Annual Conference of the IEEE Industrial Electronics Society, IECON 2022 ; Conference date: 17-10-2022 Through 20-10-2022",

year = "2022",

doi = "10.1109/IECON49645.2022.9968765",

language = "English",

series = "IECON Proceedings (Industrial Electronics Conference)",

publisher = "IEEE Computer Society",

booktitle = "IECON 2022 - 48th Annual Conference of the IEEE Industrial Electronics Society",

}

Jung, H, Kong, T, Kang, JG & Oh, S 2022, Parametric Identification using Kernel-based Frequency Response Model with Model Order Selection based on Robust Stability. in IECON 2022 - 48th Annual Conference of the IEEE Industrial Electronics Society. IECON Proceedings (Industrial Electronics Conference), vol. 2022-October, IEEE Computer Society, 48th Annual Conference of the IEEE Industrial Electronics Society, IECON 2022, Brussels, Belgium, 17/10/22. https://doi.org/10.1109/IECON49645.2022.9968765

Parametric Identification using Kernel-based Frequency Response Model with Model Order Selection based on Robust Stability. / Jung, Hanul; Kong, Taejune; Kang, Jae Gu et al.
IECON 2022 - 48th Annual Conference of the IEEE Industrial Electronics Society. IEEE Computer Society, 2022. (IECON Proceedings (Industrial Electronics Conference); Vol. 2022-October).

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

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T1 - Parametric Identification using Kernel-based Frequency Response Model with Model Order Selection based on Robust Stability

AU - Jung, Hanul

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AU - Kang, Jae Gu

AU - Oh, Sehoon

PY - 2022

Y1 - 2022

N2 - In this paper, the parametric identification is addressed by a kernel-based model with covariance and a novel model order selection algorithm. The kernel-based model is uti-lized for training the sampled frequency response characteristics, which is insufficient for parametric identification because of noisy and discrete data. The kernel-based frequency response model improves the parametric identification by using the high covariance data. In addition, prior knowledge of the model order is essential for parametric identification. This paper proposes a novel model order selection based on the robust stability criterion of disturbance observer (DOB). The effectiveness of the proposed algorithm is verified through numerical simulations under several conditions.

AB - In this paper, the parametric identification is addressed by a kernel-based model with covariance and a novel model order selection algorithm. The kernel-based model is uti-lized for training the sampled frequency response characteristics, which is insufficient for parametric identification because of noisy and discrete data. The kernel-based frequency response model improves the parametric identification by using the high covariance data. In addition, prior knowledge of the model order is essential for parametric identification. This paper proposes a novel model order selection based on the robust stability criterion of disturbance observer (DOB). The effectiveness of the proposed algorithm is verified through numerical simulations under several conditions.

UR - https://www.scopus.com/pages/publications/85143891744

U2 - 10.1109/IECON49645.2022.9968765

DO - 10.1109/IECON49645.2022.9968765

M3 - Conference contribution

AN - SCOPUS:85143891744

T3 - IECON Proceedings (Industrial Electronics Conference)

BT - IECON 2022 - 48th Annual Conference of the IEEE Industrial Electronics Society

PB - IEEE Computer Society

T2 - 48th Annual Conference of the IEEE Industrial Electronics Society, IECON 2022

Y2 - 17 October 2022 through 20 October 2022

ER -

Parametric Identification using Kernel-based Frequency Response Model with Model Order Selection based on Robust Stability

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