Iterative methods for waveform control in magnetic measurement systems

Magnetic losses in a ferromagnetic lamination can be separated into three contributions. Bertotti theoretically assessed this distribution at the end of the 20th century in the Statistical Theory of Losses (STL), triggering significant progress in understanding the dissipation mechanisms. Recent stu...

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Bibliographic Details
Published in:IEEE transactions on instrumentation and measurement 2022-01, Vol.71, p.1-1
Main Authors: Fagan, Patrick, Ducharne, Benjamin, Zurek, Stanislaw, Domenjoud, Mathieu, Skarlatos, Anastasios, Daniel, Laurent, Reboud, Christophe
Format: Article
Language:English
Subjects:
Barkhausen effect
Control methods
Electromagnetism
Energy dissipation
Engineering Sciences
feedback
Ferromagnetism
Flux density
Iterative methods
Magnetic Barkhausen noise energy
Magnetic flux
magnetic losses
Magnetic measurement
Magnetization
waveform control
Waveforms
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Summary:Magnetic losses in a ferromagnetic lamination can be separated into three contributions. Bertotti theoretically assessed this distribution at the end of the 20th century in the Statistical Theory of Losses (STL), triggering significant progress in understanding the dissipation mechanisms. Recent studies have shown the possibility of reconstructing a hysteresis cycle from the high-frequency Barkhausen noise signal. Applying STL to the Barkhausen noise cycles has never been done before. Still, it could help establish a parallel with the measurement of the magnetization cycle versus frequency and the energy loss. However, STL analysis in its ultimate description requires sinusoidal flux density, while Barkhausen noise measurements are usually done with a constant excitation slope. Multiple magnetic flux density control methods were described in the literature and are reviewed in this manuscript. However, the Barkhausen noise context, requiring high-frequency sampling during the magnetization cycle, is more constraining. Therefore, specific performance criteria were considered, followed by numerical tests to determine the most adapted method to a Barkhausen STL description. Eventually, the Proportional Iterative Learning Control (P-ILC) gave the highest satisfaction rate and was chosen for experimental tests. Some of these experimental results are provided in the manuscript discussion together with suggestions for convergence speed improvement. It is, for instance, recommended to increase the gain near saturation, where the system response is poor.
ISSN:0018-9456
1557-9662
DOI:10.1109/TIM.2022.3199198