Artificial Neural Network-based Control of a Switched Reluctance Motor for a High-precision Positioning System

Abstract

This paper presents a approach to control a switched reluctance motor (SRM) in the context of a high-precision positioning system using artificial neural networks (ANNs). The SRM is known for its robustness and simplicity, making it suitable for various applications, including positioning systems where precision is paramount. Traditional control methods often struggle to achieve the desired level of accuracy due to the non-linear and dynamic nature of the SRM. In this study, we propose an advanced control strategy leveraging the adaptive learning capabilities of ANNs. The neural network is trained to capture the intricate relationships between the motor's inputs and outputs, allowing for precise control in real-time. By measuring the electromagnetic torque and phase currents, the neural network is able to estimate the rotor position, facilitating the elimination of the rotor position sensor. The training data set of the neural network consists of magnetization data for the SRM with the electromagnetic torque and current as inputs and the corresponding position as outputs in this set. With a sufficiently large training data set, the artificial neural networks (ANN) can be correlated for appropriate network architecture.