Tracking the voltage envelope of 5G base station power supplies poses a significant long-term challenge. To achieve fast and accurate envelope tracking, an efficient and high-frequency power electronics circuit and an excellent voltage controller are essential. This paper presents a novel machine learning (ML) based feedforward control method for precise voltage reference tracking. The method is exemplified using a standard buck converter in both continuous conduction mode (CCM) and discontinuous conduction mode (DCM). A feedforward neural network (FNN) is trained to capture non-ideal and non-linear effects in real circuits. An automated data collection system, developed with MATLAB and Simulink, facilitates data acquisition and model training process. The trained FNN predicts the duty cycle for the buck converter to generate the targeted output voltage and output power. A simulation model is built to validate the effectiveness of the FNN controller in envelope tracking applications.
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