Methodological Comparison and Analysis for Six-Switching PMBLDC Motor Control

Permanent Magnet Brushless Direct Current (PMBLDC) motor plays a significant role in our daily activities and its area of application is enormous. It can be seen in the areas of robotics, military, aerospace, domestic, and industrial machine among others. Hence, a precise approach to PMBLDC motor control is imperative. This study compares and evaluate three control strategy employing Machine Learning (ML), Response Optimizer (RO), and PID controller to attain the most suitable and effective PMBLDC motor control. A six-switching driver circuit was designed to drive the motor. The simulated result was analyzed both graphically and analytically. Some differences were observed in terms of overshoot, undershoot, rise, and settling time, however, all three-control approaches RO, ML, and PID follow the reference tracking.