Optimization of Hysteresis Parameters for the Jiles-Atherton Model Using a Genetic Algorithm

Cao, Shuying; Wang, Boweng; Yan, Rongge; Huang, Wenmei; Yang, Qingxin

doi:10.1109/tasc.2004.830462

Cited by 35 publications

(16 citation statements)

References 6 publications

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“…• The second stage: the iterative procedure was used to determine the parameters k, c, a and α. It was repeated to achieve the lowest possible error ϵ (21). The determined parameters are independent of the current magnetic field value.…”

Section: Parameter Identificationmentioning

confidence: 99%

“…Other parameters such as a, α and M s were accepted as constant. The procedure was repeated to obtain the lowest possible error ϵ (21).…”

Section: Parameter Identificationmentioning

confidence: 99%

“…Other authors have proposed methods to achieve the best parameters set for the J-A model. Among them, optimization algorithms [17][18][19] or genetic algorithm [20,21] were used. Moreover, others have tried to find dependences between the J-A model parameters and magnetic field strength [22][23][24][25][26][27][28][29], microstructural parameters [30] or temperature [31].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Numerical modeling of static hysteresis loop using variable parameters

Gmyrek

2013

Int J Numerical Modelling

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The application of the Jiles–Atherton (J–A) hysteresis model for numerical modeling of measured quasistatic hysteresis loops of non‐oriented soft magnetic material is investigated. The J–A model used assumes its parameters are dependent on actual effective magnetic field strength. This is carried out by using a new parameter associated with the mathematical description of the J–A model. In this paper, the author demonstrates that these varying parameters and the proposed algorithm of parameter estimation are useful in accurate numerical modeling of the static hysteresis loop. Results of modeling are compared with experimental measurements, demonstrating very good agreement in a wide range of maximum field strength. Copyright © 2013 John Wiley & Sons, Ltd.

show abstract

Section: Parameter Identificationmentioning

confidence: 99%

“…Other parameters such as a, α and M s were accepted as constant. The procedure was repeated to obtain the lowest possible error ϵ (21).…”

Section: Parameter Identificationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Numerical modeling of static hysteresis loop using variable parameters

Gmyrek

2013

Int J Numerical Modelling

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“…However, the initial values of the parameters and the iteration order of the parameters have great influence on the method, which leads to low identification accuracy. In recent years, many intelligent optimization algorithms have been developed rapidly, and have been increasingly introduced into J-A model parameter identification, including particle swarm optimization, genetic algorithm, and neural network (Wilson et al, 2001;Salvini and Fulginei, 2002;Cao et al, 2004;Leite et al, 2004;Marion et al, 2008;Trapanese, 2011).…”

Section: Introductionmentioning

confidence: 99%

Research on Ferromagnetic Hysteresis of a Magnetorheological Fluid Damper

Gong

2019

Front. Mater.

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The inherent hysteresis of magnetorheological fluid dampers is one of the main reasons which limit their applications. The hysteresis mainly caused from two aspects. One part of the hysteresis is between the damping force and the piston velocity, which induced from the friction force of the damper, the compressibility of the fluid, rheological behavior etc. Another part of the hysteresis is between the damping force and the control current, which induced from the ferromagnetic materials inside the MR fluid damper. The ferromagnetic hysteresis of the MR fluid damper has been paid little attention to for a long time. Currently, the MR fluid damper is applying to the field of high velocity or shock and impact loadings where ferromagnetic hysteresis reduces the performance of the control current which leads to worse performances of vibration or buffer. Hall sensors are embedded to the MR fluid damper in this paper so that the magnetic flux density of the damping channels can be measured in real time. The hysteresis loops of the damping channels are obtained by measuring the relationships of the magnetic flux densities and the control currents. Furthermore, a Jiles-Atherton (J-A) hysteresis model based on differential equations for the MR fluid damper is established. The J-A hysteresis model is according to the domain-wall theory so that it has clear physical meaning with a small number of parameters. The hysteresis model is simulated utilizing MATLAB/SIMULINK. The particle swarm optimization (PSO) is adopted to identify the parameters of the J-A hysteresis model. The results show that the hysteresis loops identified by PSO is more similar to the measured hysteresis loops compared with the traditional parameter identification method. The research in this paper on the characteristic and model of the ferromagnetic hysteresis of the MR fluid damper is benefit to decrease or eliminate the effects of hysteresis which can improve the performances of the MR fluid dampers.

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“…Other authors have proposed methods to achieve the best parameters set for the J–A model. Among them, optimization algorithms or genetic algorithm was used. Moreover, others have tried to find dependences between the J–A model parameters and magnetic field strength , microstructural parameters , or temperature .…”

Section: Introductionmentioning

confidence: 99%

Static hysteresis loop model with changing parameters

Gmyrek

2013

Int. Trans. Electr. Energ. Syst.

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SUMMARY The application of the Jiles–Atherton (J–A) hysteresis model for numerical modeling of measured quasistatic hysteresis loops of non‐oriented soft magnetic material is investigated. The J–A model used assumes its parameters are dependent on actual effective magnetic field strength. This is performed by using a new parameter associated with the mathematical description of the J–A model. In this paper, the author demonstrates that these varying parameters and the proposed algorithm of parameter estimation are useful in accurate numerical modeling of the static hysteresis loop. Results of modeling are compared with experimental measurements, demonstrating very good agreement in a wide range of maximum field strength. Copyright © 2013 John Wiley & Sons, Ltd.

show abstract

Optimization of Hysteresis Parameters for the Jiles-Atherton Model Using a Genetic Algorithm

Cited by 35 publications

References 6 publications

Numerical modeling of static hysteresis loop using variable parameters

Numerical modeling of static hysteresis loop using variable parameters

Research on Ferromagnetic Hysteresis of a Magnetorheological Fluid Damper

Static hysteresis loop model with changing parameters

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