Predicting Torque Ripple and Average Torque of a Switched Reluctance Motor Using MLP and RBF Models

Hemmati, Siroos; Goldasteh, Milad; Asadi, Mohsen; Hosseini, Seyed Shahrooz; Roshani, Sobhan

doi:10.22060/eej.2026.25442.5934

Predicting Torque Ripple and Average Torque of a Switched Reluctance Motor Using MLP and RBF Models

Articles in Press

Document Type : Research Article

Authors

Siroos Hemmati ¹

Milad Goldasteh ²

Mohsen Asadi ³

Seyed Shahrooz Hosseini ⁴

Sobhan Roshani ⁴

¹ Kermanshah University of Technology, Kermanshah, Iran

² Technical National University, Kermanshah, Iran

³ Razi university, Kermanshah, Iran

⁴ Azad University, Kermanshah, Iran

10.22060/eej.2026.25442.5934

Abstract

The optimal design of a Switched Reluctance Motor (SRM) requires an accurate model. Analytical models presented for SRM do not meet required accuracy. Approximate models also have varying degrees of accuracy in predicting SRM characteristics. Hence, in this paper, two Neural Network (NN) models, Radial Basis Function (RBF) and Multilayer Perceptron (MLP), have been proposed to predict torque ripple and average torque, respectively. To train and test the models, 100 samples were extracted, 90% for training and 10% for testing. Furthermore, the Finite Element Method (FEM) has been used to solve the samples. The influencing parameters of the proposed NN models (number of hidden layers, number of neurons in hidden layers, bias, etc.) also have been determined to achieve the desired accuracy and minimal complexity. To evaluate the performance of the models, two criteria, Root Mean Square Error (RMSE) and Mean Relative Error (MRE), have been used. Both criteria indicate that the MLP model is successful in predicting torque ripple, while the RBF model excels in predicting average torque.

Keywords

Switched Reluctance Motor

Torque

RBF

MLP

Neural Network

Subjects