Enhanced Performance Analysis of ANFIS-Based Control for Fuel Cell Integrated Boost Converter

Abstract

The integration of fuel cells in renewable energy applications requires efficient power conversion to address their inherently low voltage output. This study proposes an Adaptive Neuro-Fuzzy Inference System (ANFIS) controller for a fuel cell-integrated boost converter to enhance voltage regulation, efficiency, and transient response. The ANFIS-based controller dynamically adapts to system variations, ensuring optimal performance under different load conditions. Comparative analysis with conventional PID and fuzzy logic controllers highlights the superior capabilities of the ANFIS approach, including reduced overshoot, faster settling time, and improved steady-state stability. Simulation results validate the effectiveness of the proposed controller, demonstrating its potential for reliable and efficient fuel cell power conversion.