The increasing global demand for sustainable and efficient energy conversion systems has intensified research into photovoltaic (PV) optimization techniques, particularly solar tracking mechanisms. Dual-axis solar tracking systems (DASTSs) have demonstrated superior energy harvesting capability compared to fixed-tilt systems by continuously aligning photovoltaic modules perpendicular to incident solar radiation. However, conventional tracking methods often suffer from limitations such as mechanical complexity, control instability, and suboptimal adaptability under dynamic environmental conditions. To address these challenges, this study proposes an intelligent fuzzy logic-based control framework for enhancing the operational efficiency of dual-axis solar tracking systems.

The proposed framework integrates fuzzy logic control (FLC) principles to manage uncertainties in solar irradiance, environmental disturbances, and sensor inaccuracies. The theoretical foundation is based on fuzzy set theory introduced by Mamdani, enabling approximate reasoning for nonlinear and imprecise solar tracking environments (Mamdani, 1974). The system design builds upon prior advancements in microcontroller-based and algorithm-driven tracking systems, incorporating adaptive intelligence for improved tracking precision (Akbar et al., 2017; Das et al., 2015).

A comparative synthesis of existing tracking methodologies reveals that fuzzy logic-based systems outperform traditional rule-based and mechanical tracking systems in energy efficiency, response time, and adaptability. Simulation-based evaluations and system-level conceptual modeling indicate that fuzzy logic enhances angular positioning accuracy and reduces steady-state error under varying solar trajectories. The study further contextualizes its framework within established solar energy optimization literature (Chang, 2009; Chen et al., 2005), emphasizing the integration of intelligent control for next-generation photovoltaic systems.

The findings suggest that fuzzy logic-based dual-axis tracking significantly improves energy yield efficiency and system robustness. The proposed model contributes to sustainable energy research by offering a scalable, intelligent, and cost-effective solution for solar energy harvesting optimization.