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The topology and zero current switching technique of the S\u2010DAB converter play crucial roles in enhancing the overall performance and increasing the efficiency. This study employs optimization algorithms such as the neural network controller (NNC), sliding mode controller (SMC), genetic algorithm (GA), firefly algorithm (FA), and particle swarm optimization (PSO) for effective voltage regulation, fast transient response, and stable steady\u2010state response. The S\u2010DAB converter attained an output voltage of 350\u2009V at a current of 0.92\u2009A from the input voltage of 20\u2009V at a current of 17\u2009A and achieved an efficiency of 94.07%. The S\u2010DAB converter was designed and simulated using MATLAB Simulink, and its performance metrics was measured and analyzed for different optimization algorithms. The hardware prototype of the S\u2010DAB converter was implemented using a FPGA controller with different optimization algorithms to provide better voltage regulation and to enhance the converter\u2032s time\u2010domain values. The converter\u2032s simulation and experimental results were compared, validated, and analyzed, showing close agreement in performance metrics.<\/jats:p>","DOI":"10.1155\/jama\/5513346","type":"journal-article","created":{"date-parts":[[2025,12,5]],"date-time":"2025-12-05T12:49:51Z","timestamp":1764938991000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Performance Analysis of FPGA\u2010Based Semi\u2010Dual Active Bridge DC\u2010DC Boost Converter Using Artificial Intelligence Optimization 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