A Robust Fractional-Order Nonsingular Terminal Sliding Mode Control With Deep Learning-Based Lie Derivative Estimation for Maximum Power Point Tracking in Wind Turbine
This paper presents a Robust Fractional-Order Sliding Mode Control (FOSMC) with Nonsingular Integral Terminal Dynamics, integrated with Densely Connected Convolutional Networks (DenseNet) for Lie Derivatives Estimation, to achieve Maximum Power Point Tracking (MPPT) in Wind Energy Conversion Systems...
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2025-01-01
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| Online Access: | https://ieeexplore.ieee.org/document/11072148/ |
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| author | Ahmed S. Alsafran Safeer Ullah Ameen Ullah Ghulam Hafeez Muhammad Zeeshan Babar Baheej Alghamdi Abdullah A. Algethami |
| author_facet | Ahmed S. Alsafran Safeer Ullah Ameen Ullah Ghulam Hafeez Muhammad Zeeshan Babar Baheej Alghamdi Abdullah A. Algethami |
| author_sort | Ahmed S. Alsafran |
| collection | DOAJ |
| description | This paper presents a Robust Fractional-Order Sliding Mode Control (FOSMC) with Nonsingular Integral Terminal Dynamics, integrated with Densely Connected Convolutional Networks (DenseNet) for Lie Derivatives Estimation, to achieve Maximum Power Point Tracking (MPPT) in Wind Energy Conversion Systems (WECS) based on Permanent Magnet Synchronous Generators (PMSG). The proposed method effectively addresses the challenges of nonlinear dynamics, uncertain wind conditions, and chattering effects, which are common in traditional control approaches. The core innovation lies in fractional-order sliding mode control, which enhances convergence speed and robustness while ensuring finite-time stability. Unlike classical Sliding Mode Control (SMC), the proposed Nonsingular Terminal Sliding Mode (NTSM) formulation eliminates singularities and improves tracking accuracy. Additionally, to overcome inaccuracies in numerical differentiation, a Densely Connected Convolutional Neural Network (DenseNet) is employed to estimate higher-order Lie derivatives, providing real-time system state approximation and improving control precision. A rigorous Lyapunov stability theorem guarantees the finite-time convergence of the considered system. Extensive MATLAB/Simulink simulations validate the effectiveness of the proposed control law by comparing it with the existing classical controllers. The results demonstrate superior MPPT efficiency, faster transient response, reduced chattering, and enhanced robustness under varying wind conditions. |
| format | Article |
| id | doaj-art-62b2b5425e514b1bb9903bc7dd72e596 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-62b2b5425e514b1bb9903bc7dd72e5962025-08-20T03:56:04ZengIEEEIEEE Access2169-35362025-01-011312742312743510.1109/ACCESS.2025.358627911072148A Robust Fractional-Order Nonsingular Terminal Sliding Mode Control With Deep Learning-Based Lie Derivative Estimation for Maximum Power Point Tracking in Wind TurbineAhmed S. Alsafran0Safeer Ullah1https://orcid.org/0000-0001-8017-7006Ameen Ullah2https://orcid.org/0000-0002-0993-8586Ghulam Hafeez3https://orcid.org/0000-0002-9398-9414Muhammad Zeeshan Babar4https://orcid.org/0000-0003-2427-9643Baheej Alghamdi5Abdullah A. Algethami6Department of Electrical Engineering, College of Engineering, King Faisal University, Al-Ahsa, Saudi ArabiaDepartment of Electrical Engineering, Quaid-e-Azam College of Engineering and Technology, Sahiwal, PakistanCollege of Mechatronics and Control Engineering, Shenzhen University, Shenzhen, ChinaDepartment of Electrical Engineering, University of Engineering and Technology, Mardan, PakistanSchool of Engineering and Physical Sciences, Heriot-Watt University, Edinburgh, U.K.Center of Research Excellence in Renewable Energy and Power Systems, King Abdulaziz University, Jeddah, Saudi ArabiaDepartment of Mechanical Engineering, College of Engineering, Taif University, Taif, Saudi ArabiaThis paper presents a Robust Fractional-Order Sliding Mode Control (FOSMC) with Nonsingular Integral Terminal Dynamics, integrated with Densely Connected Convolutional Networks (DenseNet) for Lie Derivatives Estimation, to achieve Maximum Power Point Tracking (MPPT) in Wind Energy Conversion Systems (WECS) based on Permanent Magnet Synchronous Generators (PMSG). The proposed method effectively addresses the challenges of nonlinear dynamics, uncertain wind conditions, and chattering effects, which are common in traditional control approaches. The core innovation lies in fractional-order sliding mode control, which enhances convergence speed and robustness while ensuring finite-time stability. Unlike classical Sliding Mode Control (SMC), the proposed Nonsingular Terminal Sliding Mode (NTSM) formulation eliminates singularities and improves tracking accuracy. Additionally, to overcome inaccuracies in numerical differentiation, a Densely Connected Convolutional Neural Network (DenseNet) is employed to estimate higher-order Lie derivatives, providing real-time system state approximation and improving control precision. A rigorous Lyapunov stability theorem guarantees the finite-time convergence of the considered system. Extensive MATLAB/Simulink simulations validate the effectiveness of the proposed control law by comparing it with the existing classical controllers. The results demonstrate superior MPPT efficiency, faster transient response, reduced chattering, and enhanced robustness under varying wind conditions.https://ieeexplore.ieee.org/document/11072148/Fractional-order sliding modenonsingular terminal sliding modewind energy systemmaximum power point trackingrenewable energymachine learning |
| spellingShingle | Ahmed S. Alsafran Safeer Ullah Ameen Ullah Ghulam Hafeez Muhammad Zeeshan Babar Baheej Alghamdi Abdullah A. Algethami A Robust Fractional-Order Nonsingular Terminal Sliding Mode Control With Deep Learning-Based Lie Derivative Estimation for Maximum Power Point Tracking in Wind Turbine IEEE Access Fractional-order sliding mode nonsingular terminal sliding mode wind energy system maximum power point tracking renewable energy machine learning |
| title | A Robust Fractional-Order Nonsingular Terminal Sliding Mode Control With Deep Learning-Based Lie Derivative Estimation for Maximum Power Point Tracking in Wind Turbine |
| title_full | A Robust Fractional-Order Nonsingular Terminal Sliding Mode Control With Deep Learning-Based Lie Derivative Estimation for Maximum Power Point Tracking in Wind Turbine |
| title_fullStr | A Robust Fractional-Order Nonsingular Terminal Sliding Mode Control With Deep Learning-Based Lie Derivative Estimation for Maximum Power Point Tracking in Wind Turbine |
| title_full_unstemmed | A Robust Fractional-Order Nonsingular Terminal Sliding Mode Control With Deep Learning-Based Lie Derivative Estimation for Maximum Power Point Tracking in Wind Turbine |
| title_short | A Robust Fractional-Order Nonsingular Terminal Sliding Mode Control With Deep Learning-Based Lie Derivative Estimation for Maximum Power Point Tracking in Wind Turbine |
| title_sort | robust fractional order nonsingular terminal sliding mode control with deep learning based lie derivative estimation for maximum power point tracking in wind turbine |
| topic | Fractional-order sliding mode nonsingular terminal sliding mode wind energy system maximum power point tracking renewable energy machine learning |
| url | https://ieeexplore.ieee.org/document/11072148/ |
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