Hybrid Backstepping–P&O Control Strategy for Enhanced MPPT in Photovoltaic Systems under Climatic Variations

Hybrid Backstepping–P&O Control Strategy for Enhanced MPPT in Photovoltaic Systems under Climatic Variations

Fatna BDAIDI, Driss YOUSFI, Ayoub RAHMOUNI

Abstract. A compact hybrid MPPT method for standalone PV systems is proposed in this paper, wherein a slow P&O-based estimator and a fast nonlinear backstepping controller design of the DC–DC boost converter are combined. The reference power is modulated for environmental variations in the P&O stage, and the backstepping loop ensures high-bandwidth, Lyapunov-stable tracking of the operating point. The proposed hybrid architecture is compared to the standalone P&O and to the standalone backstepping and it provides enhancement in steady-state ripple as well as transient performance under irradiance steps. The amount of extracted quantitative performance values from simulation waveforms indicate MPPT efficiency does not fall below 99% under steady condition, faster settling (≈0.15 s after steps) and less ripple compared to classical P&O. The method remains computationally cheap and is made more robust to fast irradiance changes.

Keywords
Photovoltaic Systems, MPPT, Backstepping, Perturb and Observe, Hybrid Control

Published online 4/25/2026, 8 pages
Copyright © 2026 by the author(s)
Published under license by Materials Research Forum LLC., Millersville PA, USA

Citation: Fatna BDAIDI, Driss YOUSFI, Ayoub RAHMOUNI, Hybrid Backstepping–P&O Control Strategy for Enhanced MPPT in Photovoltaic Systems under Climatic Variations, Materials Research Proceedings, Vol. 64, pp 115-122, 2026

DOI: https://doi.org/10.21741/9781644904091-15

The article was published as article 15 of the book Energy Futures

Content from this work may be used under the terms of the Creative Commons Attribution 3.0 license. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.

References
[1] A. Rahmouni, D. Yousfi, A. El Houre, M. Bakhouya, M. Chaker, and M. Bachiri, “Sustainable modernization of rural agricultural power systems in North Africa via optimal renewable microgrid design,” Results Eng., vol. 29, p. 109238, Mar. 2026. https://doi.org/10.1016/j.rineng.2026.109238
[2] S. Dong and Z. Wu, “Prescribed performance adaptive MPPT control of direct-drive wave energy generation system,” Ocean Eng., vol. 298, p. 117066, Apr. 2024. https://doi.org/10.1016/j.oceaneng.2024.117066
[3] T. Chen, A. Harrison, N. H. Alombah, M. Aurangzeb, A. A. Telba, and H. A. Mahmoud, “Leveraging MPPT capability for solar irradiance estimation: H-INC-IBS-based assessment of explicit models under real-world climatic conditions,” Comput. Electr. Eng., vol. 118, p. 109366, Aug. 2024. https://doi.org/10.1016/j.compeleceng.2024.109366
[4] A. Rahmouni, D. Yousfi, M. Bachiri, M. Bakhouya, and A. Rochd, “Fuzzy Logic-Based Energy Management System for an AC Microgrid,” in Digital Technologies and Applications, S. Motahhir and B. Bossoufi, Eds., Cham: Springer Nature Switzerland, 2024, pp. 434–443. https://doi.org/10.1007/978-3-031-68675-7_41
[5] R. Alik and A. Jusoh, “An enhanced P&O checking algorithm MPPT for high tracking efficiency of partially shaded PV module,” Sol. Energy, vol. 163, pp. 570–580, Mar. 2018. https://doi.org/10.1016/j.solener.2017.12.050
[6] U. Yadav, A. Gupta, and R. K. Ahuja, “Hardware validation of hybrid MPPT technique via Novel ML controller and P&O method,” Energy Rep., vol. 8, pp. 77–84, Dec. 2022. https://doi.org/10.1016/j.egyr.2022.10.067
[7] J. Chen et al., “Irradiance sensorless PSO-based Integral Backstepping and Immersion & invariance algorithm for robust MPPT control with real-climatic microcontroller-in-the-loop experimental validation,” Comput. Electr. Eng., vol. 123, p. 110279, Apr. 2025. https://doi.org/10.1016/j.compeleceng.2025.110279
[8] J. Águila-León, C. Vargas-Salgado, D. Díaz-Bello, and C. Montagud-Montalvá, “Optimizing photovoltaic systems: A meta-optimization approach with GWO-Enhanced PSO algorithm for improving MPPT controllers,” Renew. Energy, vol. 230, p. 120892, 2024. https://doi.org/https://doi.org/10.1016/j.renene.2024.120892
[9]A. Rahmouni, D. Yousfi, M. Bachiri, M. Bakhouya, and A. Rochd, “Efficient Energy Management in DC Microgrids Using Fuzzy Logic Approach,” in Proceedings of the 4th International Conference on Electronic Engineering and Renewable Energy Systems—Volume 1, B. Hajji, A. Gagliano, A. Mellit, A. Rabhi, and M. Calì, Eds., Singapore: Springer Nature, 2025, pp. 289–298. doi: 10.1007/978-981-96-0644-3_26
[10] H. Doubabi, I. Salhi, M. Chennani, and N. Essounbouli, “High Performance MPPT based on TS Fuzzy–integral backstepping control for PV system under rapid varying irradiance—Experimental validation,” ISA Trans., vol. 118, pp. 247–259, Dec. 2021. https://doi.org/10.1016/j.isatra.2021.02.004
[11] X. Lin and Y. Wu, “Parameters identification of photovoltaic models using niche-based particle swarm optimization in parallel computing architecture,” Energy, vol. 196, p. 117054, Apr. 2020. https://doi.org/10.1016/j.energy.2020.117054
[12] B. Fatna, D. Yousfi and A. Rahmouni, “Optimization of Photovoltaic Systems Under Partial Shading: A Comparative Analysis of PSO and P&O Algorithms,” 2025 International Conference on Circuit, Systems and Communication (ICCSC), Fez, Morocco, 2025, pp. 1-7. https://doi.org/10.1109/ICCSC66714.2025.11135125
[13] K. Ali, L. Khan, Q. Khan, S. Ullah, and N. Ali, “Neurofuzzy robust backstepping based MPPT control for photovoltaic system,” Turk. J. Electr. Eng. Comput. Sci., vol. 29, no. 1, pp. 421–436, Jan. 2021. https://doi.org/10.3906/elk-1907-15
[14] H. Yatimi, Y. Ouberri, S. Chahid, and E. Aroudam, “Control of an Off-Grid PV System based on the Backstepping MPPT Controller,” Procedia Manuf., vol. 46, pp. 715–723, 2020. https://doi.org/10.1016/j.promfg.2020.03.101