Beyond Nyquist in Frequency Response Function Identification: Applied to Slow-Sampled Systems

M.J. van Haren; Leonid Mirkin; Lennart L.G. Blanken; Tom A.E. Oomen

doi:10.1109/LCSYS.2023.3284344

Beyond Nyquist in Frequency Response Function Identification: Applied to Slow-Sampled Systems

M.J. van Haren (Corresponding author), Leonid Mirkin, Lennart L.G. Blanken, Tom A.E. Oomen

Research output: Contribution to journal › Article › Academic › peer-review

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Abstract

Fast-sampled models are essential for control design, e.g., to address intersample behavior. The aim of this letter is to develop a non-parametric identification technique for fast-sampled models of systems that have relevant dynamics and actuation above the Nyquist frequency of the sensor, such as vision-in-the-loop systems. The developed method assumes smoothness of the frequency response function, which allows to disentangle aliased components through local models over multiple frequency bands. The method identifies fast-sampled models of slowly-sampled systems accurately in a single identification experiment. Finally, an experimental example demonstrates the effectiveness of the technique.

Original language	English
Article number	10146423
Pages (from-to)	2131-2136
Number of pages	6
Journal	IEEE Control Systems Letters
Volume	7
DOIs	https://doi.org/10.1109/LCSYS.2023.3284344
Publication status	Published - 8 Jun 2023
Event	62nd IEEE Conference on Decision and Control, CDC 2023 - Singapore, Singapore Duration: 13 Dec 2023 → 15 Dec 2023 Conference number: 62

Keywords

Behavioral sciences
Discrete Fourier transforms
Frequency response
Frequency response function
Frequency-domain analysis
Linear systems
System identification
Transient analysis
sampled-data systems
system identification

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Cite this

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AB - Fast-sampled models are essential for control design, e.g., to address intersample behavior. The aim of this letter is to develop a non-parametric identification technique for fast-sampled models of systems that have relevant dynamics and actuation above the Nyquist frequency of the sensor, such as vision-in-the-loop systems. The developed method assumes smoothness of the frequency response function, which allows to disentangle aliased components through local models over multiple frequency bands. The method identifies fast-sampled models of slowly-sampled systems accurately in a single identification experiment. Finally, an experimental example demonstrates the effectiveness of the technique.

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Beyond Nyquist in Frequency Response Function Identification: Applied to Slow-Sampled Systems

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