ADVANCED DUAL-AXIS SOLAR TRACKING USING A NOVEL ARTIFICIAL NEURAL NETWORK-PID CONTROL STRATEGY (REVUE ROUMAINE DES SCIENCES TECHNIQUES — SÉRIE ÉLECTROTECHNIQUE ET ÉNERGÉTIQUE, 2026)
This paper addresses the challenge of enhancing dual-axis solar tracking systems' adaptability and precision under dynamic conditions. A novel hybrid control approach is proposed, combining an artificial neural network (ANN) with a proportional-integral-derivative (PID) controller. The ANN, trained using the Levenberg-Marquardt algorithm, adaptively tunes the PID gains (Kp, Ki, Kd) in real-time. Although Model Predictive Control (MPC) is currently recognized as the most advanced and effective strategy for solar tracking, our comparative study shows that the ANN-PID controller achieves faster dynamic response, improved adaptability to disturbances, and reduced computational complexity. MATLAB-Simulink simulation results validate the superior tracking accuracy and energy capture performance of the ANN-PID system, as confirmed by MAE. These findings highlight the ANN-PID control as a promising real-time alternative to MPC, offering robust and efficient solar tracking with lower computational demands.
Keywords: Artificial neural network; Solar tracking system; Model predictive control; Adaptive control; Proportional-integral-derivative (PID) controller