Dynamic switching control of buck converters using unsupervised machine learning methods

Abstract

This study presents the implementation of a new unsupervised machine learning based system called a buck converter controller using unsupervised machine learning (ABCML) to control the operation of a type of switching voltage regulators, commonly called buck converters. The system uses the voltage and current state variables of buck converters to evaluate four switching-based clustering algorithms, namely a Gaussian mixture model, a self-organising mapping (SOM), a k-means clustering and a hierarchical clustering algorithm. Step response results of the controller implementation with the buck converter show that the SOM controller improves the performance of the voltage regulator system by 99.77% in terms of voltage overshoot and by 63.24% in terms of fall time, whereas the hierarchical clustering algorithm improves the settling time of the output voltage by 3.91%. This finding of optimal machine learning implementation approaches and their comparison could be used to improve the stability and the performance of switching voltage regulation systems which are widely used in electronic systems of today.

Keywords

• learning (artificial intelligence)
• pattern clustering
• self-organising feature maps
• voltage regulators
• gaussian processes
• unsupervised learning
• switching systems (control)
• voltage control
• power convertors
• control engineering computing
• dynamic switching control
• buck converters
• unsupervised machine learning
• voltage regulators
• switching-based clustering algorithms
• gaussian mixture model
• hierarchical clustering algorithm
• controller implementation
• buck converter show
• som controller
• voltage regulator system
• voltage overshoot
• output voltage
• optimal machine
• voltage regulation systems
• electronic systems