Open AccessDynamic switching control of buck converters using unsupervised machine learning methods
Author(s) -
Abegaz Brook W.
Publication year - 2020
Publication title -
the journal of engineering
Language(s) - English
Resource type - Journals
ISSN - 2051-3305
DOI - 10.1049/joe.2020.0086
Subject(s) - computer science , cluster analysis , overshoot (microwave communication) , settling time , control theory (sociology) , buck converter , converters , controller (irrigation) , voltage , artificial intelligence , control engineering , control (management) , engineering , step response , telecommunications , agronomy , electrical engineering , biology
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.