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In the last year, artificial intelligence has grown in popularity and is now being used in a variety of fields. Either be a well-known tool in the industry. This paper discusses two critical controllers that are used to regulate the speed of various motors in order to achieve the desired and recommended speed. These controllers are the Fuzzy Logic Controller (FLC), which employs fuzzy set theory, and the Artificial Neural Network Controller (ANNC), which is based on human brain logic. They are used to control the efficient and high-performance Brushless DC motor (BLDC). Because it does not have brushes like a DC motor, this motor requires no maintenance. This motor has become more efficient as a result of these controllers. Where they minimize settling time, eliminate peak time, and overshoot of system response speed.

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A Comparative Study of Neural Network and Fuzzy Logic Controller Approaches for BLDC Motor Speed Control

  • 会议论文

  • 发表日期：

    2025年

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  • Meriem Megrini,
  • Ahmed Gaga,
  • Youness Mehdaoui

摘要

In the last year, artificial intelligence has grown in popularity and is now being used in a variety of fields. Either be a well-known tool in the industry. This paper discusses two critical controllers that are used to regulate the speed of various motors in order to achieve the desired and recommended speed. These controllers are the Fuzzy Logic Controller (FLC), which employs fuzzy set theory, and the Artificial Neural Network Controller (ANNC), which is based on human brain logic. They are used to control the efficient and high-performance Brushless DC motor (BLDC). Because it does not have brushes like a DC motor, this motor requires no maintenance. This motor has become more efficient as a result of these controllers. Where they minimize settling time, eliminate peak time, and overshoot of system response speed.