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

Khalil Chnini (), Mahamadou Abdou Tankari (), Houda Jouini, Hatem Allagui, Mostafa Ahmed Ibrahim and Ezzeddine Touti ()
Additional contact information
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

Energies, 2025, vol. 18, issue 10, 1-23

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.

Keywords: processor in the loop; MPPT; MBD; Backstepping; FTSC; ANFIS; photovoltaic system (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1996-1073/18/10/2470/pdf (application/pdf)
https://www.mdpi.com/1996-1073/18/10/2470/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:18:y:2025:i:10:p:2470-:d:1653620

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().