Dynamic Ferromagnetic Hysteresis Modelling Using a Preisach-Recurrent Neural Network Model

Abstract

In this work, a Preisach-recurrent neural network model is proposed to predict the dynamic hysteresis in ARMCO pure iron, an important soft magnetic material in particle accelerator magnets. A recurrent neural network coupled with Preisach play operators is proposed, along with a novel validation method for the identification of the model’s parameters. The proposed model is found to predict the magnetic flux density of ARMCO pure iron with a Normalised Root Mean Square Error (NRMSE) better than 0.7%, when trained with just six different hysteresis loops. The model is evaluated using ramp-rates not used in the training procedure, which shows the ability of the model to predict data which has not been measured. The results demonstrate that the Preisach model based on a recurrent neural network can accurately describe ferromagnetic dynamic hysteresis when trained with a limited amount of data, showing the model’s potential in the field of materials science.

High-Performance Tracking for Piezoelectric Actuators Using Super-Twisting Algorithm Based on Artificial Neural Networks

“…In this research, the architecture used for dynamic mapping was a Layer Recurrent Neural Network (LRNN), which is a shallow type with a recurrent inner connection and correlated with a tap delay; this feature allows the usage of previous states and present inputs to produce outputs within hidden states [83]. These ANNs kinds were proved to be efficient for modeling and mapping hysteresis phenomenon [84]. In the following analysis, tests were performed employing the Deep Learning Toolbox of MATLAB 2020a (which was compatible with the version used of dSpace); thus, the implementation allows only shallow ANNs for code generation in Simulink [85].…”

Development of a Real-Time Magnetic Field Measurement System for Synchrotron Control

“…This is especially true for history-dependent features, such as the remanent field or the response to minor hysteresis loops, as illustrated by the failure of an early linear dynamic model tested in the PSB [46]. Different classes of hysteresis models are discussed widely in the literature, including closed-form or differential analytic expressions, operator-based and neural network formulations, which have had some success, such as those in [47]. At present, these are still being evaluated to identify the most suitable one for real-time FPGA implementation.…”

Drift-Free Integration in Inductive Magnetic Field Measurements Achieved by Kalman Filtering

“…Some of the aspects on which further work is planned include extending the method to long, slender induction coils such as the ones used to measure the integral field produced by accelerator magnets and developing suitable formulations for non-linear current-to-field relationships (Equation ( 14 )) in order to improve accuracy when only the excitation current can be used. In particular, preliminary investigations showed promising results with respect to the application of recurrent neural networks [ 34 ] or more complex neural architectures [ 35 ] in terms of representing dynamic magnetic phenomena such as eddy currents and hysteresis. The use of high-precision measurements obtained by the Hall probe-based Kalman filter in a controlled laboratory setting is currently being considered for training a network that will then be used to supplement simple current measurement in a real-time operational context.…”