@article {10.3844/jcssp.2026.378.388,
article_type = {journal},
title = {Comparison of an AI-Based Controller for a Positive Output Superlift DC-DC Converter},
author = {Adlakha, Richa and Grover, Ashish and Chaudhary, Neha and Mahajan, Rashima and Bhatia, Sunny},
volume = {22},
number = {2},
year = {2026},
month = {Feb},
pages = {378-388},
doi = {10.3844/jcssp.2026.378.388},
url = {https://thescipub.com/abstract/jcssp.2026.378.388},
abstract = {DC-DC converters have emerged as crucial components in various industrial applications and form an integral part of power supplies based on switch-mode. Solar energy has garnered increasing significance owing to the scarcity of non-renewable fuels and its abundant availability. Photovoltaic (PV) technology, characterized by the arrangement of numerous series and parallel-connected PV cells within modules, generates DC output. Consequently, the demand for DC-DC converters has risen to elevate voltage to desired levels and optimize solar power output. While conventional boost converters have gained prominence, they exhibit limited gain and introduce voltage ripples, necessitating the addition of filters. This, however, results in increased system size and cost. To address these challenges, this paper introduces a topology for  DC-DC conversion that is based on the voltage lift technique, which is capable of geometrically progressing voltage enhancement. Closed-loop control becomes indispensable to enhance system efficiency and stability. This paper employs optimized proportional-integral controllers and intelligent controllers like fuzzy logic and artificial neural networks to precisely regulate the positive output converters. With these controllers' use, the system's dynamic response has been improved.},
journal = {Journal of Computer Science},
publisher = {Science Publications}
}