Akademik Veri Yönetim Sistemi
Signal, Image and Video Processing, cilt.19, sa.1, 2025 (SCI-Expanded)
Brushless Direct Current (BLDC) motor control in drones and unmanned aerial vehicles is critical for safety, performance, and high precision. In this study, a method based on machine learning rather than traditional methods is proposed to automatically control a system using a BLDC. An experimental system using a brushless direct current motor used in unmanned aerial vehicles was designed and a data set was created with the control studies. For the obtained data set, the Proportional- Integral- Derivative (PID) values were changed at certain intervals and the error values that occurred when applied to the system were recorded. The PID parameters obtained by seven different machine learning methods and the traditional method are compared. The performances of the machine learning methods were evaluated using regression estimation error metrics. According to the results obtained, Kp, Ki, and Kd values were applied to the system. The system response to sine input and step input is compared. When all machine learning experiments were evaluated, the Stochastic Gradient Descent (SGD) method was the most successful method, achieving 99.988% prediction success according to the R2 metric. When the results are analyzed, it is concluded that the system can be successfully controlled automatically using machine learning techniques.