A Neural Network-Based Controller for DC-DC Buck Converter for Improved Resilience

This paper presents a novel Neural Network (NN) based control strategy for DC-DC Buck converters aiming to enhance the dynamic system response in face of external disturbances. The proposed controller employs a compact multilayer perceptron, and it is trained online through supervised learning strategy. Its ground-truth labels correspond to the optimal control actions that minimize the converter output voltage error. This results in a tiny NN, paving the way for embedded hardware implementations. The validation of the proposed NN-based controller is conducted via circuital simulations, demonstrating significant improvements in transient response compared to conventional control techniques.