ANFIS-Based Duty-Cycle Control for a Non-Isolated Interleaved Bidirectional DC–DC Converter in Hybrid PV–Battery Systems
In this paper, an Adaptive Neuro-Fuzzy Inference System (ANFIS)-based duty-cycle correction method is proposed for a non-isolated interleaved bidirectional DC–DC converter used in a hybrid photovoltaic (PV)–battery system. The ANFIS controller was developed using training data generated from an optimized conventional fuzzy-logic controller operating in both buck and boost modes. The main objective of the proposed control strategy is to produce a smoother duty-cycle response, improve transient behavior, and maintain better output-voltage stability than the conventional fuzzy-logic approach. The converter and control system were modeled and tested in MATLAB/Simulink under constant-voltage and constant-current operating conditions. The simulation results indicate that the proposed ANFIS-based controller improves the converter's dynamic response and provides smoother duty-cycle adjustment. In buck mode, the output-voltage ripple is reduced from 0.1181 V to 0.1051 V, indicating a modest improvement. A more significant improvement is observed in boost mode, where the voltage ripple decreases from 2.963 V to 1.348 V. The results indicate that the proposed ANFIS controller works effectively in boost mode, particularly given its greater sensitivity to duty-cycle changes, switching dynamics, and transient disturbances.