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Maximum power point tracking (MPPT) is a technique that allows photovoltaic (PV) arrays to operate at or near their maximum power point (MPP) under all weather conditions. Fuzzy logic controller (FLC) is the most popular method to monitor the MPPT, such as Type-1 fuzzy logic (T1FL), Type-2 fuzzy logic (T2FL), and Type-3 fuzzy logic (T3FL) controllers. This article proposes a new T3FL algorithm that improves tracking efficiency and tracking speed because it is better equipped to deal with uncertainty arising from disturbances. The proposed system consists of a PV module, a DC-DC buck converter controlled by the MPPT algorithm, a resistive load, and a battery. The PV solar panel is directly connected to the DC-DC buck converter, which operates based on the proposed controller’s output pulse width to ensure that the PV system works at MPP. The T3FL algorithm has the advantage of extracting the maximum power from the PV panel while preventing battery damage caused by variable MPPT voltage and thus extending battery lifetime. The maximum power and voltage of T3FL are compared with the existing T1FL and T2FL of battery and ohmic load. The MPPT performance of the proposed different algorithms was applied to a PV module and a DC-DC buck converter to be evaluated  via  simulation studies and experimental studies. MATLAB/Simulink is used to implement different algorithms. Several weather conditions are simulated: (i) uniform irradiation, (ii) sudden changing, and (iii) partial shading. A real-time implementation of the three MPPT algorithms by using Arduino Uno and spatial Simulink package known as the “support package for Arduino hardware”. Finally, the simulation and experimental results demonstrate that the T3FL controller of an ohmic load provides accurate maximum power, high levels of stability, and robust performance against uncertainties arising from disturbances to the PV system’s inputs

Maximum Power Point Tracking for Solar Photovoltaic System Based on Interval Type-3 Fuzzy Logic: Practical Validation