A Time-Frequency Local Polynomial Approach to FRM Estimation from Incomplete Data

N.J. Dirkx; Koen Tiels; Tom Oomen

doi:10.1016/j.ifacol.2023.10.1332

A Time-Frequency Local Polynomial Approach to FRM Estimation from Incomplete Data

N.J. Dirkx, Koen Tiels, Tom Oomen

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Abstract

Frequency Response Matrix (FRM) estimation from measured data is an important step towards the control of complex systems, including motion and thermal systems. Missing samples in the measured data records, e.g., due to sensor failure or faulty data transmission, often occur. In this paper, a method is presented for the nonparametric FRM identification of multiple-inputs multiple-outputs (MIMO) systems from incomplete and noisy data records. The method exploits time- and frequency-domain localizing wavelets to accurately estimate the FRM and its covariance from the time-frequency plane. Good performance is demonstrated in a simulation study.

Original language	English
Pages (from-to)	3942-3947
Number of pages	6
Journal	IFAC-PapersOnLine
Volume	56
Issue number	2
DOIs	https://doi.org/10.1016/j.ifacol.2023.10.1332
Publication status	Published - 1 Jul 2023
Event	22nd World Congress of the International Federation of Automatic Control (IFAC 2023 World Congress) - Yokohama, Japan Duration: 9 Jul 2023 → 14 Jul 2023 Conference number: 22 https://www.ifac2023.org/

Funding

This work was supported by the Research Programme VIDI under Project 15698, partly financed by the NWO.

Funders	Funder number
Nederlandse Organisatie voor Wetenschappelijk Onderzoek

Keywords

Frequency response function identification
linear systems
missing data
multiple-inputs multiple-outputs
transient estimation

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10.1016/j.ifacol.2023.10.1332

DirkxTieOom2023Final author version , 1.08 MBLicence: CC BY-NC-ND
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Cite this

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abstract = "Frequency Response Matrix (FRM) estimation from measured data is an important step towards the control of complex systems, including motion and thermal systems. Missing samples in the measured data records, e.g., due to sensor failure or faulty data transmission, often occur. In this paper, a method is presented for the nonparametric FRM identification of multiple-inputs multiple-outputs (MIMO) systems from incomplete and noisy data records. The method exploits time- and frequency-domain localizing wavelets to accurately estimate the FRM and its covariance from the time-frequency plane. Good performance is demonstrated in a simulation study.",

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AB - Frequency Response Matrix (FRM) estimation from measured data is an important step towards the control of complex systems, including motion and thermal systems. Missing samples in the measured data records, e.g., due to sensor failure or faulty data transmission, often occur. In this paper, a method is presented for the nonparametric FRM identification of multiple-inputs multiple-outputs (MIMO) systems from incomplete and noisy data records. The method exploits time- and frequency-domain localizing wavelets to accurately estimate the FRM and its covariance from the time-frequency plane. Good performance is demonstrated in a simulation study.

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KW - linear systems

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A Time-Frequency Local Polynomial Approach to FRM Estimation from Incomplete Data

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