Dynamic Preisach Hysteresis Model for Magnetostrictive Materials for Energy Application

Abstract:

In this paper the magnetostrictive material considered is Terfenol-D. Its hysteresis is modeled by applying the DPM whose identification procedure is performed by using a neural network procedure previously publised [. The neural network used is a multiplayer perceptron trained with the Levenberg-Marquadt training algorithm. This allows to obtain the Preisach distribution function, without any special conditioning of the measured data, owing to the filtering capabilities of the neural network interpolators. The model is able to reconstruct both the magnetization relation and the Field-strain relation. The model is validated through comparison and prediction of data collected from a typical Terfenol-D transducer.

• Periodical: Applied Mechanics and Materials (Volume 432)
• Pages: 72-77
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.432.72
• Online since: September 2013
• Authors: G. La Rocca, V. Franzitta, Alessia Viola, Marco Trapanese
• Keywords: Dynamic Preisach Model, Electric Machine, Magnetic Hysteresis, Power System

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