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Embedded Processor-in-the-Loop Implementation of ANFIS-Based Nonlinear MPPT Strategies for Photovoltaic Systems

Author

Listed:
  • Khalil Chnini

    (Laboratory of Energy Applications and Renewable Energy Efficiency (LAPER), Faculty of Sciences of Tunis, El Manar University, Tunis 1068, Tunisia)

  • Mahamadou Abdou Tankari

    (CERTES Laboratory, University Paris-Est Creteil (UPEC), 94010 Creteil, France)

  • Houda Jouini

    (Laboratory of Energy Applications and Renewable Energy Efficiency (LAPER), Faculty of Sciences of Tunis, El Manar University, Tunis 1068, Tunisia)

  • Hatem Allagui

    (Laboratory of Energy Applications and Renewable Energy Efficiency (LAPER), Faculty of Sciences of Tunis, El Manar University, Tunis 1068, Tunisia)

  • Mostafa Ahmed Ibrahim

    (Department of Electrical Engineering, University of Business and Technology, Jeddah 21432, Saudi Arabia)

  • Ezzeddine Touti

    (Center for Scientific Research and Entrepreneurship, Northern Border University, Arar 73213, Saudi Arabia)

Abstract

The integration of photovoltaic (PV) systems into global energy production is rapidly expanding. However, achieving maximum power extraction remains a significant challenge due to the nonlinear electrical characteristics of PV modules, which are highly sensitive to environmental variations such as temperature fluctuations and irradiance changes. This study presents a structured design, testing, and quasi-experimental validation methodology for robust Maximum Power Point Tracking (MPPT) control in PV systems. We propose two advanced AI-based nonlinear control strategies: an Adaptive Neuro-Fuzzy Inference System combined with Fast Terminal Synergetic Control (ANFIS-FTSC) for a boost converter and ANFIS with Backstepping (ANFIS-BS) for a Single-Ended Primary Inductor Converter (SEPIC), both of which have demonstrated tracking efficiencies exceeding 99.6%. To evaluate real-time performance, a Processor-in-the-Loop (PIL) validation is conducted using an ARM-based STM32F407VG microcontroller. The methodology adheres to a Model-Based Design (MBD) framework, ensuring systematic development, implementation, and verification of the MPPT algorithms in an embedded environment. Experimental results demonstrate that the proposed controllers achieve high efficiency, rapid convergence, and robust maximum power point tracking under varying operating conditions. The successful PIL-based validation confirms the feasibility of these intelligent control techniques for real-world deployment in PV energy systems, paving the way for more efficient and adaptive renewable energy solutions.

Suggested Citation

  • Khalil Chnini & Mahamadou Abdou Tankari & Houda Jouini & Hatem Allagui & Mostafa Ahmed Ibrahim & Ezzeddine Touti, 2025. "Embedded Processor-in-the-Loop Implementation of ANFIS-Based Nonlinear MPPT Strategies for Photovoltaic Systems," Energies, MDPI, vol. 18(10), pages 1-23, May.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:10:p:2470-:d:1653620
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    References listed on IDEAS

    as
    1. Alberto Coronado-Mendoza & Mónica Camas-Náfate & Jesús Sergio Artal-Sevil & José Antonio Domínguez-Navarro, 2025. "Optimum Design of a Photovoltaic Inverter System Based on Ga, Pso and Gwo Algorithms with a Mppt Sliding Mode Control," Energies, MDPI, vol. 18(8), pages 1-16, April.
    2. Zou, Ling & Wang, Lunche & Xia, Li & Lin, Aiwen & Hu, Bo & Zhu, Hongji, 2017. "Prediction and comparison of solar radiation using improved empirical models and Adaptive Neuro-Fuzzy Inference Systems," Renewable Energy, Elsevier, vol. 106(C), pages 343-353.
    3. Muhammad Mateen Afzal Awan & Muhammad Yaqoob Javed & Aamer Bilal Asghar & Krzysztof Ejsmont, 2022. "Performance Optimization of a Ten Check MPPT Algorithm for an Off-Grid Solar Photovoltaic System," Energies, MDPI, vol. 15(6), pages 1-31, March.
    4. Kamran Ali & Laiq Khan & Qudrat Khan & Shafaat Ullah & Saghir Ahmad & Sidra Mumtaz & Fazal Wahab Karam & Naghmash, 2019. "Robust Integral Backstepping Based Nonlinear MPPT Control for a PV System," Energies, MDPI, vol. 12(16), pages 1-20, August.
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