Adaptive Control of a Boost DC/DC Converter based on Artificial Intelligence

The paper presents a solution to model and control the operation of DC/DC converters, in normal regime based on Artificial Intelligence. A solution is developed for simulating and controlling the performance of DC/DC converters, encompassing both normal and fault scenarios. The suggested model for the converter employs a transfer function mathematical model configuration with adaptable coefficients. To accommodate variable duty cycles, fully connected neural networks are utilized to determine the appropriate coefficients for the model. Additionally, a control framework capable of identifying faults and mitigating their impact is presented. This framework includes a compensator designed to prevent unstable conditions as the converter parameters deviate from their nominal values, serving as an effective fault tolerance mechanism The proposed methodology is aimed at devising algorithms suitable for real-time execution on 32-bit ARM processors. Copyright (c) 2024 The Authors.