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

Nic 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

Nic Dirkx^*, Koen Tiels, Tom Oomen

^*Corresponding author for this work

Team Jan-Willem van Wingerden

Research output: Contribution to journal › Conference article › Scientific › peer-review

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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 - 2023
Event	22nd IFAC World Congress - Yokohama, Japan Duration: 9 Jul 2023 → 14 Jul 2023

Funding

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

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

1-s2.0-S240589632301738X-mainFinal published version, 727 KBLicence: CC BY-NC-ND

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title = "A Time-Frequency Local Polynomial Approach to FRM Estimation from Incomplete Data",

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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language = "English",

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AU - Dirkx, Nic

AU - Tiels, Koen

AU - Oomen, Tom

PY - 2023

Y1 - 2023

N2 - 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.

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.

KW - Frequency response function identification

KW - linear systems

KW - missing data

KW - multiple-inputs multiple-outputs

KW - transient estimation

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DO - 10.1016/j.ifacol.2023.10.1332

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T2 - 22nd IFAC World Congress

Y2 - 9 July 2023 through 14 July 2023

ER -

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

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