A Uniform Robust Exact Differentiator Based Neuro-Fuzzy Fractional Order Sliding Mode Control for Optimal Standalone Solar Photovoltaic System
This study introduces an innovative neurofuzzy fractional-order sliding mode control approach for standalone photovoltaic systems, designed to mitigate uncertainties and disturbances caused by fluctuating environmental conditions. The method combines a fuzzy logic neural network, uniform robust exac...
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| Format: | Article |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10823094/ |
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| author | Safeer Ullah Ahmed S. Alsafran Ambe Harrison Ghulam Hafeez Abouobaida Hassan Baheej Alghamdi Habib Kraiem |
| author_facet | Safeer Ullah Ahmed S. Alsafran Ambe Harrison Ghulam Hafeez Abouobaida Hassan Baheej Alghamdi Habib Kraiem |
| author_sort | Safeer Ullah |
| collection | DOAJ |
| description | This study introduces an innovative neurofuzzy fractional-order sliding mode control approach for standalone photovoltaic systems, designed to mitigate uncertainties and disturbances caused by fluctuating environmental conditions. The method combines a fuzzy logic neural network, uniform robust exact differentiator, and fractional-order sliding mode control. The neural network accurately predicts nonlinear reference voltage trajectories, whereas the differentiator estimates unmeasurable states and external disturbances. The inclusion of fractional-order control improved the adaptability and robustness of the system. The stability of the proposed approach is rigorously validated using the Lyapunov theory. MATLAB simulations and experimental results significantly improve tracking accuracy and overall system performance, providing a robust and efficient solution to optimize energy extraction in standalone PV systems. |
| format | Article |
| id | doaj-art-ee1a872a08fb4c10b31f299fadf65bcd |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-ee1a872a08fb4c10b31f299fadf65bcd2025-01-10T00:01:31ZengIEEEIEEE Access2169-35362025-01-01134411442310.1109/ACCESS.2024.352488710823094A Uniform Robust Exact Differentiator Based Neuro-Fuzzy Fractional Order Sliding Mode Control for Optimal Standalone Solar Photovoltaic SystemSafeer Ullah0https://orcid.org/0000-0001-8017-7006Ahmed S. Alsafran1https://orcid.org/0000-0003-3669-6161Ambe Harrison2https://orcid.org/0000-0002-4353-1261Ghulam Hafeez3https://orcid.org/0000-0002-9398-9414Abouobaida Hassan4Baheej Alghamdi5https://orcid.org/0000-0002-9569-4724Habib Kraiem6https://orcid.org/0000-0003-3248-2430Department of Electrical Engineering, Quaid-e-Azam College of Engineering and Technology, Sahiwal, PakistanDepartment of Electrical Engineering, King Faisal University, Hofuf, Alahssa, Saudi ArabiaDepartment of Electrical and Electronics Engineering, University of Buea, Buea, CameroonDepartment of Electrical Engineering, University of Engineering and Technology, Mardan, PakistanLaboratory of Engineering Sciences for Energy (LABSIPE), National School of Applied Sciences (ENSA) of El Jadida, Chouaib-Doukkali University, El Jadida, MoroccoSmart Grids Research Group, Center of Research Excellence in Renewable Energy and Power Systems, King Abdulaziz University, Jeddah, Saudi ArabiaCenter for Scientific Research and Entrepreneurship, Northern Border University, Arar, Saudi ArabiaThis study introduces an innovative neurofuzzy fractional-order sliding mode control approach for standalone photovoltaic systems, designed to mitigate uncertainties and disturbances caused by fluctuating environmental conditions. The method combines a fuzzy logic neural network, uniform robust exact differentiator, and fractional-order sliding mode control. The neural network accurately predicts nonlinear reference voltage trajectories, whereas the differentiator estimates unmeasurable states and external disturbances. The inclusion of fractional-order control improved the adaptability and robustness of the system. The stability of the proposed approach is rigorously validated using the Lyapunov theory. MATLAB simulations and experimental results significantly improve tracking accuracy and overall system performance, providing a robust and efficient solution to optimize energy extraction in standalone PV systems.https://ieeexplore.ieee.org/document/10823094/Fractional order sliding moderobust controladaptive controluniform robust exact differentiatorenergy extractionphotovoltaic system |
| spellingShingle | Safeer Ullah Ahmed S. Alsafran Ambe Harrison Ghulam Hafeez Abouobaida Hassan Baheej Alghamdi Habib Kraiem A Uniform Robust Exact Differentiator Based Neuro-Fuzzy Fractional Order Sliding Mode Control for Optimal Standalone Solar Photovoltaic System IEEE Access Fractional order sliding mode robust control adaptive control uniform robust exact differentiator energy extraction photovoltaic system |
| title | A Uniform Robust Exact Differentiator Based Neuro-Fuzzy Fractional Order Sliding Mode Control for Optimal Standalone Solar Photovoltaic System |
| title_full | A Uniform Robust Exact Differentiator Based Neuro-Fuzzy Fractional Order Sliding Mode Control for Optimal Standalone Solar Photovoltaic System |
| title_fullStr | A Uniform Robust Exact Differentiator Based Neuro-Fuzzy Fractional Order Sliding Mode Control for Optimal Standalone Solar Photovoltaic System |
| title_full_unstemmed | A Uniform Robust Exact Differentiator Based Neuro-Fuzzy Fractional Order Sliding Mode Control for Optimal Standalone Solar Photovoltaic System |
| title_short | A Uniform Robust Exact Differentiator Based Neuro-Fuzzy Fractional Order Sliding Mode Control for Optimal Standalone Solar Photovoltaic System |
| title_sort | uniform robust exact differentiator based neuro fuzzy fractional order sliding mode control for optimal standalone solar photovoltaic system |
| topic | Fractional order sliding mode robust control adaptive control uniform robust exact differentiator energy extraction photovoltaic system |
| url | https://ieeexplore.ieee.org/document/10823094/ |
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