Artificial Neural Networks Based Analysis of BLDC Motor Speed Control

• URL: http://arxiv.org/abs/2108.12320v1
• Date: Fri, 27 Aug 2021 14:55:10 GMT
• Title: Artificial Neural Networks Based Analysis of BLDC Motor Speed Control
• Authors: Porselvi T, Sai Ganesh CS, and Aouthithiye Barathwaj SR Y
• Abstract summary: Brushless Direct Current motor (BLDC motor) uses electronic closed-loop controllers to switch DC current to the motor windings and produces the magnetic fields. BLDC motor finds various applications owing to its high speed, low maintenance and adequate torque capability. This article presents a method of speed control of BLDC motors where speed is controlled by changing the DC input voltage of the bridge converter.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Artificial Neural Network (ANN) is a simple network that has an input, an output, and numerous hidden layers with a set of nodes. Implementation of ANN algorithms in electrical, and electronics engineering always satisfies with the expected results as ANN handles binary data more accurately. Brushless Direct Current motor (BLDC motor) uses electronic closed-loop controllers to switch DC current to the motor windings and produces the magnetic fields. The BLDC motor finds various applications owing to its high speed, low maintenance and adequate torque capability. They are highly preferred than the other motors because of their better performance and it is easy to control their speed by Power Converters. This article presents a method of speed control of BLDC motors where speed is controlled by changing the DC input voltage of the bridge converter that feeds the motor winding. The control is done by using a PI based speed controller. The motor is modeled in the MATLAB/Simulink and the speed control is obtained with a PI controller. EMF signals, rotor speed, electromagnetic torque, Hall Effect signals, PWM and EMF signals simulations are then obtained. This acquired data is then fed into binary artificial neural networks and as a result, the ANN model predicts the corresponding parameters close to the simulation results. Both the mathematical based simulation and data based prediction gives satisfactory results

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