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

Journal Article (2023)
Author(s)

Nic Dirkx (Eindhoven University of Technology, ASML)

Koen Tiels (Eindhoven University of Technology)

Tom Oomen (Eindhoven University of Technology, TU Delft - Team Jan-Willem van Wingerden)

Research Group
Team Jan-Willem van Wingerden
DOI related publication
https://doi.org/10.1016/j.ifacol.2023.10.1332
More Info
expand_more
Publication Year
2023
Language
English
Research Group
Team Jan-Willem van Wingerden
Issue number
2
Volume number
56
Pages (from-to)
3942-3947
Reuse Rights

Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.

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.