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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

References

Al-Othman, A., Younes, T., Al-Adwan, I., Al Khawaldah, M., Alauthman, H., Alkhedher, M., & Ramadan, M. (2023). An experimental study on hybrid control of a solar tracking system to maximize energy harvesting in Jordan. Solar Energy, 263, 111931.
Al-Quraan, A., Al-Mahmodi, M., Al-Asemi, T., Bafleh, A., Bdour, M., Muhsen, H., & Malkawi, A. (2022). A New Configuration of Roof Photovoltaic System for Limited Area Applications—A Case Study in KSA. Buildings, 12(2), 92.
Aslam, S., Chak, Y.-C., Hussain Jaffery, M., Varatharajoo, R., & Ahmad Ansari, E. (2023). Model predictive control for Takagi–Sugeno fuzzy model-based Spacecraft combined energy and attitude control system. Advances in Space Research, 71(10), 4155-4172.
Aung, C. A., Hote, Y. V., Pillai, G., & Jain, S. (2020). PID Controller Design for Solar Tracker via Modified Ziegler Nichols Rules. 2020 2nd International Conference on Smart Power & Internet Energy Systems (SPIES), 531-536.
Away, Y., Novandri, A., Raziah, I., & Melinda, M. (2023). A New Technique for Improving the Precision of Dual-Axis Tetrahedron-Based Sensor with Switching Between PID and ANN. IEEE Access, 11, 89138-89151.
Bin Ishak, M. H., Burham, N., Masrie, M., Janin, Z., & Sam, R. (2023). Automatic Dual-Axis Solar Tracking System for Enhancing the Performance of a Solar Photovoltaic Panel. 2023 IEEE 9th International Conference on Smart Instrumentation, Measurement and Applications (ICSIMA), 279-283.
Chanfreut, P., Maestre, J. M., Gallego, A. J., Annaswamy, A. M., & Camacho, E. F. (2023). Clustering-based model predictive control of solar parabolic trough plants. Renewable Energy, 216, 118978.
De Oliveira, R. A., Ronnberg, S. K., & Bollen, M. H. J. (2022). Third Harmonic and its Relation to Solar Elevation Angle in a PV Installation with Solar Tracking Systems. 2022 20th International Conference on Harmonics & Quality of Power (ICHQP), 1-6.
Dousoky, G. M., El-Sayed, A.-H. M., & Shoyama, M. (2012). Increasing energy-efficiency in solar radiation trackers for photovoltaic arrays. 2012 IEEE Energy Conversion Congress and Exposition (ECCE), 4113-4120.
Fuentes-Morales, R. F., Diaz-Ponce, A., Peña-Cruz, M. I., Rodrigo, P. M., Valentín-Coronado, L. M., Martell-Chavez, F., & Pineda-Arellano, C. A. (2020). Control algorithms applied to active solar tracking systems: A review. Solar Energy, 212, 203-219.
Ikhwan, M., Mardlijah, & Imron, C. (2018). Model predictive control on dual axis solar tracker using Matlab/Simulink simulation. 2018 International Conference on Information and Communications Technology (ICOIACT), 784-788.
Ishrat, Z., Gupta, A.K., & Nayak, S. (2024). Optimizing Photovoltaic Arrays: A Novel Approach to Maximize Power Output in Varied Shading Patterns. Journal of Renewable Energy and Environment, 11(2), 75–88.
Ishrat, Z., Ali, K.B., Vats, S., & Kumar, S. (2023). Optimizing Solar Energy Harvesting: Supervised Machine Learning-Driven Peak Power Point Tracking for Diverse Weather Conditions. International Journal of Robotics and Control Systems, 3(4), 1007–1020.
Jha, S. K., Roy, S., Singh, V. K., & Mishra, D. P. (2020). Sun's Position Tracking by Solar Angles Using MATLAB. In 2020 International Conference on Renewable Energy Integration into Smart Grids, pp.5-9.
Jamroen, C., Fongkerd, C., Krongpha, W., Komkum, P., Pirayawaraporn, A., & Chindakham, N. (2021). A novel UV sensor-based dual-axis solar tracking system: Implementation and performance analysis. Applied Energy, 299, 117295.
Kumar, A., Saini, H.K., Dubey, A.K., & DĂ­az, V.G. (Eds.). (2024). Bio-Inspired Data-driven Distributed Energy in Robotics and Enabling Technologies (1st ed.). CRC Press.
Lopez-Sanchez, I., & Moreno-Valenzuela, J. (2023). PID control of quadrotor UAVs: A survey. Annual Reviews in Control, 56, 100900.
Masoumi, A. P., Bagherian, V., Tavakolpour-Saleh, A. R., & Masoomi, E. (2023). A new two-axis solar tracker based on the online optimization method: Experimental investigation and neural network modeling. Energy and AI, 14, 100284.
Meng, Y., & Fan, C. (2023). Hybrid Systems Neural Control with Region-of-Attraction Planner (arXiv:2303.10327). arXiv.
Mouloodi, S., Rahmanpanah, H., Gohari, S., Burvill, C., & Davies, H. M. S. (2022). Feedforward backpropagation artificial neural networks for predicting mechanical responses in complex nonlinear structures: A study on a long bone. Journal of the Mechanical Behavior of Biomedical Materials, 128, 105079.
Ngo, H. C., Hachim, A. R., Ikram, R. R. R., Salahuddin, L., & Cheong, H. J. (2020). Design of Single and Dual-axis Solar Tracker System using Neural Network. International Journal of Advanced Trends in Computer Science and Engineering, 9(5), 7992-7997.
Palomino-Resendiz, S. I., Ortiz-MartĂ­nez, F. A., Paramo-Ortega, I. V., GonzĂĄlez-Lira, J. M., & Flores-HernĂĄndez, D. A. (2023). Optimal Selection of the Control Strategy for Dual-Axis Solar Tracking Systems. IEEE Access, 11, 56561-56573.
Pirayawaraporn, A., Sappaniran, S., Nooraksa, S., Prommai, C., Chindakham, N., & Jamroen, C. (2023). Innovative sensorless dual-axis solar tracking system using particle filter. Applied Energy, 338, 120946.

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