Engineering World
E-ISSN: 2692-5079 An Open Access, Peer Reviewed Journal of Selected Publications in Engineering and Applied Sciences
Volume 6, 2024
Artificial Neural Network-based Control of a Switched Reluctance Motor for a High-precision Positioning System
Authors: ,
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.
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Keywords: Switched Reluctance Motor (SRM), Rotor Position, artificial neural networks, and
electromagnetic torque
Pages: 136-143
DOI: 10.37394/232025.2024.6.14