Güngör, MustafaAsker, Mehmet Emin2023-12-142023-12-142023Güngör M, Asker ME. Learning-Based Approaches for Voltage Regulation and Control in DC Microgrids with CPL. Sustainability. 2023; 15(21):15501. https://doi.org/10.3390/su152115501https://doi.org/10.3390/su152115501https://hdl.handle.net/20.500.12514/4700This article introduces a novel approach to voltage regulation in a DC/DC boost converter. The approach leverages two advanced control techniques, including learning-based nonlinear control. By combining the backstepping (BSC) algorithm with artificial neural network (ANN)-based control techniques, the proposed approach aims to achieve accurate voltage tracking. This is accomplished by employing the nonlinear distortion observer (NDO) technique, which enables a fast dynamic response through load power estimation. The process involves training a neural network using data from the BSC controller. The trained network is subsequently utilized in the voltage regulation controller. Extensive simulations are conducted to evaluate the performance of the proposed control strategy, and the results are compared to those obtained using conventional BSC and model predictive control (MPC) controllers. The simulation results clearly demonstrate the effectiveness and superiority of the suggested control strategy over BSC and MPC.en10.3390/su152115501info:eu-repo/semantics/openAccessANNpower estimationBSCvoltage regulationmodel predictive controlArticle1515501Q2001100368300001