<![CDATA[The sharp rise in global energy demand over recent decades has necessitated the exploration of alternative energy sources. Solar energy, known for being both pollution- and fuel-free, stands out as a preferred choice. However, its efficiency is sensitive to factors like temperature fluctuations and solar irradiation. To optimize energy extraction, a maximum power point tracking algorithm is crucial for photovoltaic systems. This paper proposes a robust sliding mode control enhanced with an artificial neural network to achieve the Maximum Power Point in a stand-alone PV system. The artificial neural network determines the reference voltage, which is then regulated by the sliding mode control to match the photovoltaic array voltage. The performance of the suggested controller is compared to that of a proportional integral-based neural network controller and the perturb and observe method using MATLAB/Simulink. The results show that the suggested method provides excellent tracking performance and rapid convergence even under quickly changing weather conditions, highlighting its efficiency and robustness.]]>
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