This paper presents a control strategy of maximum power point tracking and focuses on hybrid q-learning and type-2 fuzzy logic control strategy. Photovoltaics have non-linear voltage and current characteristics which influenced by temperature and exposure of solar irradiation so that the maximum power point (MPP) can change at any time. Unfortunately, operating conditions outside the MPP could reduce the efficiency of electrical power transfer from the photovoltaic system to the load. Q-learning (QL) has the potential to provide action decisions of duty cycle signal based on the state of the power gradient percentage range. The Q-learning hybrid method with Type-2 fuzzy logic control is proposed to provide correction of QL control signal by considering the uncertainty direction of power point shifting. The proposed strategy is important particularly to avoid a sudden change in solar irradiation exposure. The simulation results show that the Q-learning hybrid type-2 fuzzy logic control based MPPT response in the photovoltaic control system has tracking efficiency about 97%, rapid rise time of 0.08s, settling time of 0.23s, low overshoot and stable response for handling the change of irradiation and temperature exposure simultaneously.

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