SMART ANFIS-CONTROLLED AUXILIARY H-BRIDGE CONVERTER FOR ADVANCED DC/DC POWER CONVERSION

Abstract

The increasing demand for high-efficiency DC/DC power conversion in renewable energy systems, electric vehicles, and industrial power supplies necessitates the development of intelligent control techniques capable of handling nonlinear operating conditions and parameter uncertainties. This paper proposes a smart Adaptive Neuro-Fuzzy Inference System (ANFIS)-controlled Auxiliary H-Bridge converter for advanced DC/DC power conversion applications. The Auxiliary H-Bridge topology enhances converter performance by providing flexible power flow control, reduced switching stress, and improved voltage conversion capability. The proposed ANFIS controller combines the learning capability of artificial neural networks with the linguistic reasoning of fuzzy logic systems to generate optimal switching control actions under varying load and input conditions. The intelligent controller adaptively regulates the converter output voltage while minimizing overshoot, improving transient response, and maintaining high conversion efficiency. MATLAB/Simulink simulations are carried out under different operating scenarios, including load disturbances and source voltage variations. The obtained results demonstrate superior dynamic performance, improved voltage regulation, enhanced efficiency, and greater robustness compared with conventional PI-controlled converter systems. Therefore, the proposed ANFIS-controlled Auxiliary H-Bridge converter represents an effective solution for next-generation  intelligent DC/DC power conversion systems.