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All Journal Bulletin of Electrical Engineering and Informatics
Le, Anh Van
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Electrical & Electronics Engineering

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An RBF neural network–based MPPT with sliding mode and fuzzy control for PV systems using buck converter Le, Anh Van; Pham, Minh Van; Vu, Linh Thi To
Bulletin of Electrical Engineering and Informatics Vol 15, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i3.11797

Abstract

This paper proposes an integrated control strategy for maximum power point tracking (MPPT) in photovoltaic (PV) systems using a buck converter. The controller combines a radial basis function (RBF) neural network for uncertainty approximation, sliding mode control (SMC) for robustness, and fuzzy logic for adaptive tuning of the switching gain to reduce chattering. The complete RBF–SMC–fuzzy control law is derived, and closed-loop stability is proven using Lyapunov theory. Simulation results in MATLAB/Simulink under both resistive and battery charging loads show that the proposed method achieves fast tracking with a settling time of about 20 ms, a tracking efficiency higher than 99%, and a voltage ripple of approximately 1.2%. Compared with conventional methods, the proposed controller significantly reduces chattering and improves power extraction performance under irradiance and load variations.
Co-Authors Pham, Minh Van Vu, Linh Thi To