Mitigation of Applied Load Using Machine Learning for Adaptive Motor Control

This paper investigates the use of machine learning to estimate the duty cycle of a pulse-width modulated (PWM) signal for motor control applications. Emerging industry trends show a greater focus on the use of machine learning algorithms in control systems over traditional proportional-integral-derivative (PID) controllers. Results demonstrate the machine learning model’s accuracy below a targeted 3% average, highlighting the importance of optimizing model design for practical implementation.