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A particle swarm optimized Takagi-Sugeno (T-S) fuzzy logic controller for maximum power point tracking in a photovoltaic (PV) system is presented. The method proposed automates the tuning of fuzzy logic controller (FLC) rules and membership functions as opposed to the trial-and-error approach. Expert knowledge used for tuning the FLC is extracted from an improved PV module model under varying solar radiation, temperature, and load conditions. The proposed optimized FLC provides fast and accurate tracking of the maximum power point (MPP) under varying operating conditions. The formulation, implementation, and simulation results are presented. Results obtained has shown that the optimized FLC gives a better performance compared with the conventional FLC tuned using trial and error.