A neural network model of parametric nonlinear hysteretic inductors

Abstract

A neural network model of nonlinear hysteretic inductors is presented. The proposed neural network model is shown to reproduce the dynamic scalar hysteretic behavior of the current-flux relationship. Moreover, the intrinsic characteristics of the neural network approach yield a model particularly suitable when dependencies on different parameters are present, e.g., influence of mobile part positions, of temperature etc. Both theoretical comparisons (e.g., Chua-Stronsmoe model and Jiles-Atherton model) and experimental measurements (e.g., variable reluctance linear motor) are considered. Results point out the numerical accuracy and computational efficiency of the proposed NN approach, that results in a general framework useful in the field of electric machines, of electronic power circuits, of control and identification.