Supervised learning for fast inverse motor control mapping: a comparative study on SRM and BLDC motors

Abstract

This paper investigates the application of machine learning (ML) models, specifically artificial neural networks (ANN) and XGBoost, for real-time motor control, focusing on switched reluctance motors (SRM) and brushless DC motors (BLDC). Traditional inverse dynamics mapping for motor control is compared with ML approaches to highlight advantages in speed, accuracy, and deployment efficiency. Datasets simulating the input-output behavior of both motor types are used to train and test the models. Key performance metrics such as mean squared error (MSE), R² score, training time, and latency are evaluated, with the goal of replacing traditional control methods in real-time applications. Results indicate that ML models outperform traditional methods in terms of prediction accuracy and deployment speed, suggesting a promising path toward more efficient and adaptive motor control systems. The novelty of this work lies in applying supervised learning directly for inverse motor control mapping, thereby eliminating the need for explicit analytical models and enabling a unified, data-driven benchmarking framework across SRM and BLDC.