Artificial Neural Network Based On a Predictive Current Control in A DC to DC Buck Converter

Author(s)	S. Sethu
Country	India
Abstract	Artificial Neural Networks (ANNs) have emerged as a powerful tool for enhancing predictive control strategies in power electronics applications. This paper presents an ANN-based predictive current control approach for a DC-DC buck converter, aimed at improving dynamic performance and efficiency. The proposed method utilizes a trained neural network model to predict the optimal duty cycle for the converter, based on real-time input voltage, load conditions, and current feedback. By leveraging machine learning techniques, the system can achieve faster transient response, reduced steady-state error, and enhanced robustness against parameter variations compared to conventional control methods like PI controllers.
Field	Engineering
Published In	Volume 16, Issue 1, January-March 2025
Published On	2025-03-10
Cite This	Artificial Neural Network Based On a Predictive Current Control in A DC to DC Buck Converter - S. Sethu - IJSAT Volume 16, Issue 1, January-March 2025. DOI 10.71097/IJSAT.v16.i1.2354
DOI	https://doi.org/10.71097/IJSAT.v16.i1.2354
Short DOI	https://doi.org/g87rhf

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