A8.1 - A Preisach Based Model for the Characterisation of Magnetic Hysteresis (original) (raw)
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A Preisach-based hysteresis model for magnetic and ferroelectric hysteresis
Applied Physics A, 2010
In this paper we present a model for hysteretic nonlinearities with non-local memories. This model can be applied to describe hysteretic material behavior. Common applications are ferromagnetic or ferroelectric materials. Our model consists of an analytic function and a Preisach operator. We define a continuous Preisach weight function and introduce a method for the identification of the model parameters. The model parameters are customized to a set of symmetric hysteresis curves. We verify our model for a soft magnetic material, a hard magnetic material and the ferroelectric behavior of some piezoelectric material. After that, non-symmetric curves like the virgin curve are predicted very well by the model. It is especially useful, if forced magnetization or polarization, that appears beyond technical saturation, come into account.
Physica B: Condensed Matter, 2001
A Preisach model able to adjust to different systems with hysteresis is presented. The related identification scheme involved uses data from a major hysteresis curve and a least-squares error minimization procedure for the parameters of the characteristic density. The output sequence, f ðtÞ; is obtained by integrating the characteristic probability density function, rða; bÞ; of the elementary hysteresis operators, g ab ; operating on the input sequence uðtÞ over the Preisach plane. Once the appropriate operator is chosen and the Preisach plane adjusted accordingly, the parameters of the characteristic density are determined via a least-squares procedure minimizing the error between the experimental major curve and the calculated one. Results using two different scalar operators are discussed. Further, the reliability of the procedure is assessed by considering experimental data regarding two different magnetic samples and a shape-memory alloy sample. r 2001 Published by Elsevier Science B.V. 0921-4526/01/$ -see front matter r 2001 Published by Elsevier Science B.V. PII: S 0 9 2 1 -4 5 2 6 ( 0 1 ) 0 0 9 8 3 -8
Hysteresis Modeling and Applications
Advances in Scattering and Biomedical Engineering, 2004
Preisach modeling, long known in the area of magnetics, has introduced mathematical abstraction to the modeling of the highly nonlinear and complex phenomenon of hysteresis. The 2D Preisach-type models presented here, departing slightly from the classical formulation, waive some of its limitations while maintaining the major advantages of simplicity and speed in calculations. Results on different types of ferromagnets are shown, as well as on magnetostrictive materials and shape memory alloys.
Preisach Hysteresis Modeling and Applications
2006
Preisach modeling, long known in the area of magnetics, has introduced mathematical abstraction to the modeling of the highly nonlinear and complex phenomenon of hysteresis. The 2D Preisach-type models presented here, departing slightly from the classical formulation, waive some of its limitations while maintaining the major advantages of simplicity and speed in calculations. Results on different types of ferromagnets are shown, as well as on magnetostrictive materials and shape memory alloys.
A method for the determination of the parameters of the hysteresis model of magnetic materials
IEEE Transactions on Instrumentation and Measurement, 1994
Many methods have been proposed for the determination of the hysteresis loops of magnetic materials, and many mathematical approaches have been proposed to find a good model for the hysteresis phenomenon. However, very few attempts have been made to determine the parameters of the hysteresis model experimentally. This paper will show how, starting from a digital method for the experimental determination of the hysteresis loop under different maximum induction values, the parameters of a hysteresis model can be automatically estimated with good accuracy.
Identification of the 2D vector Preisach hysteresis model
Compel-the International Journal for Computation and Mathematics in Electrical and Electronic Engineering, 2011
The paper presents a Preisach model to simulate the vector hysteresis properties of ferromagnetic materials. The vector behavior has been studied using a single sheet tester with a round shaped specimen at low frequency, and the locus of the magnetic flux density vector has been controlled by a digital measurement system. An inverse vector Preisach hysteresis model has been developed and identified by applying the measured data. Finally, the inverse model has been inserted into a finite element procedure through the fixed point technique and the reduced magnetic scalar potential formulation to simulate the single sheet tester measurement system. The applicability of the magnetizer system as well as the developed model has been proven by comparing measured and simulated results. Keywords-inverse hysteresis characteristics, vector hysteresis, hysteresis measurement, finite element method.
Using neural networks in the identification of Preisach-type hysteresis models
IEEE Transactions on Magnetics, 1998
The identification process of the classical Preisachtype hysteresis model reduces to the determination of the weight function of elementary hysteresis operators upon which the model is built. It is well known that the classical Preisach model can exactly represent hysteretic nonlinearities which exhibit wiping-out and congruency properties. In that case, the model identification can be analytically and systematically accomplished by using first-order reversal curves. If the congruency property is not exactly valid, the Preisach model can only be used as an approximation. It is possible to improve the model accuracy in this situation by incorporating more appropriate experimental data during the identification stage. However, performing this process using the traditional systematic techniques becomes almost impossible. In this paper, the machinery of neural networks is proposed as a tool to accomplish this identification task. The suggested identification approach has been numerically implemented and carried out for a magnetic tape sample that does not possess the congruency property. A comparison between measured data and model predictions suggests that the proposed identification approach yields more accurate results.
Automatic and accurate evaluation of the parameters of a magnetic hysteresis model
IEEE Transactions on Instrumentation and Measurement, 2000
This paper presents a method based on both artificial neural networks (ANN's) and on a multidimensional optimization procedure in order to significantly reduce the time taken and to improve the accuracy in evaluating parameters of the Jiles-Atherton's model of magnetic hysteresis. The main steps of the method can be individuated as 1) data acquisition of the experimental hysteresis loop of the magnetic material under test, 2) evaluation of the model's parameters by means of ANN, and 3) parameter accuracy improvement by means of a multidimensional optimization procedure. In order to highlight the method's effectiveness, the results of numerical and experimental tests are also given. where he is currently, he is a Researcher in the Department of Electronic, Computers, and System Science. He has worked in the area of electrical and electronic circuit simulation and in the field of device modeling. His current researches include neural networks modelling for ADC and measuring systems and digital signal processing for monitoring and testing. Zilina, Slovak Republic, in 1945. He received the Ing.