Difficulty in identification of Preisach hysteresis model weighting function using first order reversal curves method in soft magnetic materials

The full material characteristics in the Preisach model of hysteresis is a two-dimensional weighting function. It can be determined experimentally from systematic measurement of partial hysteresis loops followed by derivation of their decreasing parts. Because of measurement errors, the derivation is not correct. Nevertheless, basic material features can be obtained either from incomplete measurement that uses the Preisach triangle respecting the measurement errors. The non-uniform grid in the Preisach model is used in order to get approximately the same steps in changes of the flux density. Simulation shows that this withdrawal of some rows and columns does not change the hysteresis loop considerably, only the steps of the flux density may be higher.

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“…The decrease followed by constant value at the end part of several curves in Fig. 2a was explained in [4]. The vertical cuts, in Fig.…”

Section: Measurement In the Time Domainmentioning

confidence: 75%

“…The current source was used to ensure the harmonic field strength excitation. The standard apparatus, * corresponding author; e-mail: jakub.eichler@tul.cz described in [4] was used. The measured material was a grain oriented steel with 4% of silicon that was used to create the toroid transformer.…”

Section: Measurement In the Time Domainmentioning

confidence: 99%

Experimental Determination of the Preisach Model for Grain Oriented Steel

2019

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“…These models are categorized into two groups: (i) deductive models developed based on physical laws and characterized by their complex theory (Malczyk and Izydorczyk, 2015; Padilha et al, 2017; Sima et al, 2018), and (ii) inductive models based on experimental data analysis. Examples of the second group are Preisach model (Novak et al, 2018; Nguyen et al, 2018), Bouc-Wen model (Pelliciari et al, 2018; Yu et al, 2017), Prandtl-Ishlinskii model (Ma et al, 2018a; Janaideh and Aljanaideh, 2018; Xie et al, 2018a), fuzzy model (Rebai et al, 2015d), Backlash-like model (Zhou et al, 2017) and fractional order model (Rebai et al, 2014; Zhu et al, 2018). For a detailed review on this topic see (Ikhouan, 2018; Rebai et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

Robust adaptive fuzzy sliding mode controller for nonlinear uncertain hysteretic systems

Rebai

Guesmi

2020

Transactions of the Institute of Measurement and Control

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This paper deals with the problem of adaptive fuzzy control for a class of nonlinear uncertain systems with hysteresis input. Fuzzy logic systems are employed to approximate the unknown nonlinear behaviors, and the sliding mode technique is used to synthesize an adaptive fuzzy controller. A proportional integral control term is adopted to reduce the chattering phenomenon engendered by both sliding mode control technique and hysteretic characteristic of the system. The proposed control scheme ensures the boundedness of all closed-loop signals, and forces the tracking error to converge to zero. The main contribution of this work is the development of a control strategy for a class of nonlinear hysteretic systems subject to external disturbances and uncertainties. Two case studies are given to illustrate and to prove the effectiveness of the presented approach.

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“…Nevertheless, estimating Preisach density from hysteresis loops is a non-trivial task. Common approaches involve taking numerical derivatives of measurements that may pose challenges for noisy and experimental data [15], imposing prior forms on Preisach density such as Gaussian and Factorized-Lorentzian functions [16], [17], and…”

Section: Introductionmentioning

confidence: 99%

Estimating Preisach Density via Subset Selection

Kim

Neumayer

et al. 2020

IEEE Access

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Preisach density is drawing increasing attention for interpreting material properties for memory and storage electronics. Preisach density can be linked to the observed hysteresis loops via the Preisach model that is based on the superposition of relay operators. Reconstructing Preisach density from hysteresis is an ill-posed problem with nonunique solutions. To alleviate ambiguities, we address Preisach density reconstruction as a constrained subset selection task utilizing structured sparsity regularizations. We validate our approach under various simulation settings and apply it on experimental band-excitation piezoresponse spectroscopy (BEPS) datasets to gain insights in microstructure-dependent properties of the tip-surface contact.

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Difficulty in identification of Preisach hysteresis model weighting function using first order reversal curves method in soft magnetic materials

Cited by 22 publications

References 9 publications

Experimental Determination of the Preisach Model for Grain Oriented Steel

Experimental Determination of the Preisach Model for Grain Oriented Steel

Robust adaptive fuzzy sliding mode controller for nonlinear uncertain hysteretic systems

Estimating Preisach Density via Subset Selection

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