The increasing global demand for high-efficiency photovoltaic (PV) systems has intensified research into advanced solar tracking mechanisms capable of maximizing energy harvesting under dynamic environmental conditions. This study presents a smart dual-axis photovoltaic tracking model integrating Model Predictive Control (MPC), Proportional–Integral–Derivative (PID) control, and Artificial Neural Networks (ANN) within a dynamic controller selection framework. The primary objective is to enhance energy capture efficiency by intelligently switching between control strategies based on real-time environmental and system states.

The proposed architecture leverages predictive optimization for trajectory estimation, neural adaptive learning for nonlinear environmental response modeling, and PID-based stability control for fast transient conditions. A comparative synthesis of existing hybrid tracking systems demonstrates that hybridization of control methodologies significantly improves robustness and adaptability in fluctuating irradiance environments (Al-Othman et al., 2023). Furthermore, sensor-driven and sensorless tracking strategies are analyzed to evaluate system responsiveness and energy efficiency trade-offs.

Simulation-based performance evaluation indicates that hybrid MPC–ANN–PID systems outperform conventional single-method controllers in terms of tracking accuracy, energy yield, and dynamic stability. The findings highlight the importance of adaptive control switching mechanisms in modern PV tracking systems. The study contributes a unified optimization framework for intelligent solar tracking systems suitable for large-scale renewable energy applications.