Machine Learning Based Switch-Mode DC-DC Buck Converter Power Supply

Abstract

This paper investigates the use of machine learning to estimate the duty cycle of a pulse-width modulated (PWM) signal based on a set of inputs. A switch-mode DC-DC buck converter power supply was used to generate the inputs to the algorithm. Replacing traditional controllers such as proportional-integral (PI) controllers with machine learning controllers can yield gains in real-time control system accuracy and precision. The results demonstrate the machine learning controller’s effectiveness and accuracy. The results highlighted the importance of optimizing the model to be implemented in a practical application.