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International Journal of Renewable, Green, and Sustainable Energy

Open Access Peer Review International
Open Access

Optimization and Performance Analysis of a Smart Dual-Axis Photovoltaic Tracking Model Using Predictive Control, Neural Networks, and PID-Based Dynamic Selection Framework

DOI https://doi.org/10.55640/ijrgse-v03i08-02
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Chinedu Okafor 1 , Aisha Bello 1 ,
4 Department of Electrical and Renewable Energy Engineering, University of Lagos, Nigeria
4 Centre for Green Technology Research, Ahmadu Bello University, Nigeria

Abstract

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.

Keywords

Dual-axis solar tracking, Model predictive control, Artificial neural networks, PID control

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