ADVANCED DUAL-AXIS SOLAR TRACKING USING A NOVEL ARTIFICIAL NEURAL NETWORK-PID CONTROL STRATEGY
DOI:
https://doi.org/10.59277/RRST-EE.2026.1.7Keywords:
Artificial neural network, Solar tracking system, Model predictive control, Adaptive control, Proportional-integral-derivative (PID) controllerAbstract
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.
References
(1) A. El Hammoumi, S. Chtita, S. Motahhir, A. El Ghzizal, Solar PV energy: from material to use, and the most commonly used techniques to maximize the power output of PV systems: a focus on solar trackers and floating solar panels, Energy Reports, 8, pp. 11992–12010 (2022).
(2) A. Awasthi et al., Review on sun tracking technology in solar PV system, Energy Reports, 6, pp. 392–405 (2020).
(3) H. Shang, W. Shen, Design and implementation of a dual-axis solar tracking system, Energies, 16, 17, 6330 (2023).
(4) S. Seba, M. Birane, K. Benmouiza, A comparative analysis of boost converter topologies for photovoltaic systems using MPPT (P&O) and Beta methods under partial shading, Rev. Roum. Sci. Techn. – Électrotechn. et Énerg., 68, 4, pp. 375–380 (2023).
(5) Z. Lammouchi et al., Enhanced model-free predictive control for voltage source inverters using an adaptive observer, Rev. Roum. Sci. Techn. – Électrotechn. et Énerg., 69, 3, pp. 317–322 (2024).
(6) L. Khettache et al., A novel design of a photovoltaic system based on a linear induction motor and reciprocating pump, Rev. Roum. Sci. Techn. – Électrotechn. et Énerg., 70, 1, pp. 3–8 (2025).
(7) Z. Yang, Z. Xiao, A review of the sustainable development of solar photovoltaic tracking system technology, Energies, 16, 23, 7768 (2023).
(8) S.L. Jurj, R. Rotar, Increasing the solar reliability factor of a dual-axis solar tracker using an improved online built-in self-test architecture, IEEE Access, 12, pp. 37715–37730 (2024).
(9) A. Tahtah, Z. Zahzouh, H. Kaddour, Robust solar tracking with neural network predictive modeling and sliding mode control, Journal of Renewable Energies, pp. 119–125 (2024).
(10) M. Angulo-Calderón, I. Salgado-Tránsito, I. Trejo-Zúñiga, C. Paredes-Orta, S. Kesthkar, A. Díaz-Ponce, Development and accuracy assessment of a high-precision dual-axis pre-commercial solar tracker for concentrating photovoltaic modules, Applied Sciences, 12, 5, 2625 (2022).
(11) N.G. Hariri, M.A. AlMutawa, I.S. Osman, I.K. AlMadani, A.M. Almahdi, S. Ali, Experimental investigation of azimuth- and sensor-based control strategies for a PV solar tracking application, Applied Sciences, 12, 9, 4758 (2022).
(12) M.B. Ahmad, A.S. Muhammad, H.A. Hussain, A.H. Muhammad, A.M. Sani, Review on impact of installing the solar tracking system, its challenges and types, Artificial & Computational Intelligence, pp. 1–7 (2020).
(13) C.A. Aung, Y.V. Hote, G. Pillai, S. Jain, PID controller design for solar tracker via modified Ziegler Nichols rules, 2020 2nd International Conference on Smart Power & Internet Energy Systems (SPIES), Bangkok, Thailand, pp. 531–536 (2020).
(14) W.A. Shah, M.W. Khan, R. Mansoor, Arshad, Analysis of power-efficient high torque solar tracker through PID controller, 2020 7th International Conference on Electrical and Electronics Engineering (ICEEE), Antalya, Turkey, pp. 176–180 (2020).
(15) K.S. Rao, V.N.S. Praneeth, Y.V.P. Kumar, D.J. Pradeep, Investigation on various training algorithms for robust ANN-PID controller design, 9, 2 (2020).
(16) H.S. Purnama, T. Sutikno, S. Alavandar, A.C. Subrata, Intelligent control strategies for tuning PID of speed control of DC motor – a review, 2019 IEEE Conference on Energy Conversion (CENCON), Yogyakarta, Indonesia, pp. 24–30 (2019).
(17) S.J. Hammoodi, K.S. Flayyih, A.R. Hamad, Design and implementation speed control system of DC motor based on PID control and MATLAB Simulink, International Journal of Power Electronics and Drive Systems (IJPEDS), 11, 1, pp. 127–134 (2020).
(18) M. Schwenzer, M. Ay, T. Bergs, D. Abel, Review on model predictive control: an engineering perspective, International Journal of Advanced Manufacturing Technology, 117, 5–6, pp. 1327–1349 (2021).
(19) A. Afram, F. Janabi-Sharifi, Theory and applications of HVAC control systems – a review of model predictive control (MPC), Building and Environment, 72, pp. 343–355 (2014).
(20) M.G. Forbes, R.S. Patwardhan, H. Hamadah, R.B. Gopaluni, Model predictive control in industry: challenges and opportunities, IFAC-PapersOnLine, 48, 8, pp. 531–538 (2015).
(21) O.I. Abiodun et al., Comprehensive review of artificial neural network applications to pattern recognition, IEEE Access, 7, pp. 158820–158846 (2019).
(22) H.K. Ghritlahre, R.K. Prasad, Application of ANN technique to predict the performance of solar collector systems – a review, Renewable and Sustainable Energy Reviews, 84, pp. 75–88 (2018).
(23) A. Tahtah, Z. Zahzouh, Dual-axis solar tracking technique combining MPC, PID, and ANN control with dynamic controller selection, Journal of Renewable Energy and Environment, pp. 1–10 (2025).
(24) A.R. Amelia, Y.M. Irwan, I. Safwati, W.Z. Leow, M.H. Mat, M.S.A. Rahim, Technologies of solar tracking systems: a review, IOP Conference Series: Materials Science and Engineering, 767, 1, 012052 (2020).
(25) K. Kumar, L. Varshney, A. Ambikapathy, R.K. Saket, S. Mekhilef, Solar tracker transcript—a review, International Transactions on Electrical Energy Systems, 31, 12 (2021).
(26) R.F. Fuentes-Morales et al., Control algorithms applied to active solar tracking systems: a review, Solar Energy, 212, pp. 203–219 (2020).
(27) S. Seme, B. Štumberger, M. Hadžiselimović, K. Sredenšek, Solar photovoltaic tracking systems for electricity generation: a review, Energies, 13, 16, 4224 (2020).
(28) S.A. Sadrossadat, O. Rahmani, ANN‐based method for parametric modelling and optimising efficiency, output power and material cost of BLDC motor, IET Electric Power Applications, 14, 6, pp. 951–960 (2020).
(29) T. Guillod, P. Papamanolis, J.W. Kolar, Artificial neural network (ANN) based fast and accurate inductor modeling and design, IEEE Open Journal of Power Electronics, 1, pp. 284–299 (2020).
(30) B. Prasad, R. Kumar, M. Singh, Analysis of DC motor for process control application using neural network predictive controller, Engineering Research Express, 6, 2, 025004 (2024).
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