An improvement of the Jiles-Atherton model by introducing a dynamic dependence of all five parameters is presented. The values of parameters dynamically change as a function of the magnetic field excitation. The proposed model aims to improve the poor performance of the classic model Jiles-Atherton in generalizing both saturated and minor loops of a given material. Non-linear optimization techniques such as Genetic Algorithm (GA), Trust-Region-Reflective (TRR) and Levenberg-Marquardt (LM) were used by a hybrid configuration for the parameter identification, using hysteresis loops generated with the Preisach model as target. Different cases of excitation trends are investigated and final validation and comparison results are presented.

RIGANTI FULGINEI, F., Lozito, G.M., Gaiotto, S., Salvini, A. (2015). Improving the Jiles-Atherton model by introducing a full dynamic dependence of parameters. In 2015 IEEE 1st International Forum on Research and Technologies for Society and Industry, RTSI 2015 - Proceedings (pp.161-165). Institute of Electrical and Electronics Engineers Inc. [10.1109/RTSI.2015.7325091].

Improving the Jiles-Atherton model by introducing a full dynamic dependence of parameters

RIGANTI FULGINEI, Francesco;Lozito, Gabriele Maria;SALVINI, Alessandro
2015-01-01

Abstract

An improvement of the Jiles-Atherton model by introducing a dynamic dependence of all five parameters is presented. The values of parameters dynamically change as a function of the magnetic field excitation. The proposed model aims to improve the poor performance of the classic model Jiles-Atherton in generalizing both saturated and minor loops of a given material. Non-linear optimization techniques such as Genetic Algorithm (GA), Trust-Region-Reflective (TRR) and Levenberg-Marquardt (LM) were used by a hybrid configuration for the parameter identification, using hysteresis loops generated with the Preisach model as target. Different cases of excitation trends are investigated and final validation and comparison results are presented.
9781467381666
9781467381666
RIGANTI FULGINEI, F., Lozito, G.M., Gaiotto, S., Salvini, A. (2015). Improving the Jiles-Atherton model by introducing a full dynamic dependence of parameters. In 2015 IEEE 1st International Forum on Research and Technologies for Society and Industry, RTSI 2015 - Proceedings (pp.161-165). Institute of Electrical and Electronics Engineers Inc. [10.1109/RTSI.2015.7325091].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11590/313543
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