Physics-informed NN-based adaptive backstepping terminal sliding mode control of buck converter for PEM electrolyzer
This paper proposes an advanced control approach to controlling a DC-DC buck converter for a proton exchange membrane (PEM) electrolyzer within the framework of a direct current (DC) microgrid. The proposed adaptive backstepping terminal sliding mode control (ABTSMC) leverages a physics-informed neu...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2024-04-01
|
| Series: | Heliyon |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S240584402405285X |
| _version_ | 1797214917081694208 |
|---|---|
| author | Abdullah Baraean Mahmoud Kassas Md Shafiul Alam Mohamed A. Abido |
| author_facet | Abdullah Baraean Mahmoud Kassas Md Shafiul Alam Mohamed A. Abido |
| author_sort | Abdullah Baraean |
| collection | DOAJ |
| description | This paper proposes an advanced control approach to controlling a DC-DC buck converter for a proton exchange membrane (PEM) electrolyzer within the framework of a direct current (DC) microgrid. The proposed adaptive backstepping terminal sliding mode control (ABTSMC) leverages a physics-informed neural network (PINN) to accurately estimate and compensate for system uncertainty. The composite controller achieves finite-time convergence of the tracking error by combining backstepping control and terminal sliding mode control (TSMC). The proposed PINN aims to optimize the unconstrained parameters by utilizing observed training points from the solution, ensuring the network accurately interpolates a limited portion of the solution. The efficacy of the proposed hybrid control method is validated using a hardware-in-the-loop (HIL) implementation under various test settings, ensuring the preservation of the actual performance of the PEM electrolyzer during testing. The experimental verification results demonstrate that the proposed control method exhibits greater benefits, such as a faster dynamic response and greater robustness against parameter uncertainties than improved sliding mode-based controllers. In situations where operational conditions change, a rapid response is achieved within a mere 0.025s of settling time, exhibiting a minimal percentage overshoot of about 17.5% and presenting minimal fluctuations. |
| first_indexed | 2024-04-24T11:21:47Z |
| format | Article |
| id | doaj.art-ba6d591d34b1473698781ba183e97b91 |
| institution | Directory Open Access Journal |
| issn | 2405-8440 |
| language | English |
| last_indexed | 2024-04-24T11:21:47Z |
| publishDate | 2024-04-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Heliyon |
| spelling | doaj.art-ba6d591d34b1473698781ba183e97b912024-04-11T04:41:38ZengElsevierHeliyon2405-84402024-04-01107e29254Physics-informed NN-based adaptive backstepping terminal sliding mode control of buck converter for PEM electrolyzerAbdullah Baraean0Mahmoud Kassas1Md Shafiul Alam2Mohamed A. Abido3Department of Electrical Engineering, College of Engineering and Physics, King Fahd University of Petroleum & Minerals, Dhahran 31261, Saudi ArabiaDepartment of Electrical Engineering, College of Engineering and Physics, King Fahd University of Petroleum & Minerals, Dhahran 31261, Saudi Arabia; Interdisplinary Research Center for Sustainable Energy Systems (IRC-SES), Research Institute, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia; Corresponding author. Interdisplinary Research Center for Sustainable Energy Systems (IRC-SES), Research Institute, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia.Applied Research Center for Environment and Marine Studies, Research Institute, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi ArabiaDepartment of Electrical Engineering, College of Engineering and Physics, King Fahd University of Petroleum & Minerals, Dhahran 31261, Saudi Arabia; Interdisplinary Research Center for Sustainable Energy Systems (IRC-SES), Research Institute, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia; SDAIA-KFUPM Joint Research Center for Artificial Intelligence (JRCAI), KFUPM, Saudi ArabiaThis paper proposes an advanced control approach to controlling a DC-DC buck converter for a proton exchange membrane (PEM) electrolyzer within the framework of a direct current (DC) microgrid. The proposed adaptive backstepping terminal sliding mode control (ABTSMC) leverages a physics-informed neural network (PINN) to accurately estimate and compensate for system uncertainty. The composite controller achieves finite-time convergence of the tracking error by combining backstepping control and terminal sliding mode control (TSMC). The proposed PINN aims to optimize the unconstrained parameters by utilizing observed training points from the solution, ensuring the network accurately interpolates a limited portion of the solution. The efficacy of the proposed hybrid control method is validated using a hardware-in-the-loop (HIL) implementation under various test settings, ensuring the preservation of the actual performance of the PEM electrolyzer during testing. The experimental verification results demonstrate that the proposed control method exhibits greater benefits, such as a faster dynamic response and greater robustness against parameter uncertainties than improved sliding mode-based controllers. In situations where operational conditions change, a rapid response is achieved within a mere 0.025s of settling time, exhibiting a minimal percentage overshoot of about 17.5% and presenting minimal fluctuations.http://www.sciencedirect.com/science/article/pii/S240584402405285XAdaptive backstepping terminal sliding mode controllerBuck power converter controlPEM electrolyzerPhysics-informed neural network (PINN) |
| spellingShingle | Abdullah Baraean Mahmoud Kassas Md Shafiul Alam Mohamed A. Abido Physics-informed NN-based adaptive backstepping terminal sliding mode control of buck converter for PEM electrolyzer Heliyon Adaptive backstepping terminal sliding mode controller Buck power converter control PEM electrolyzer Physics-informed neural network (PINN) |
| title | Physics-informed NN-based adaptive backstepping terminal sliding mode control of buck converter for PEM electrolyzer |
| title_full | Physics-informed NN-based adaptive backstepping terminal sliding mode control of buck converter for PEM electrolyzer |
| title_fullStr | Physics-informed NN-based adaptive backstepping terminal sliding mode control of buck converter for PEM electrolyzer |
| title_full_unstemmed | Physics-informed NN-based adaptive backstepping terminal sliding mode control of buck converter for PEM electrolyzer |
| title_short | Physics-informed NN-based adaptive backstepping terminal sliding mode control of buck converter for PEM electrolyzer |
| title_sort | physics informed nn based adaptive backstepping terminal sliding mode control of buck converter for pem electrolyzer |
| topic | Adaptive backstepping terminal sliding mode controller Buck power converter control PEM electrolyzer Physics-informed neural network (PINN) |
| url | http://www.sciencedirect.com/science/article/pii/S240584402405285X |
| work_keys_str_mv | AT abdullahbaraean physicsinformednnbasedadaptivebacksteppingterminalslidingmodecontrolofbuckconverterforpemelectrolyzer AT mahmoudkassas physicsinformednnbasedadaptivebacksteppingterminalslidingmodecontrolofbuckconverterforpemelectrolyzer AT mdshafiulalam physicsinformednnbasedadaptivebacksteppingterminalslidingmodecontrolofbuckconverterforpemelectrolyzer AT mohamedaabido physicsinformednnbasedadaptivebacksteppingterminalslidingmodecontrolofbuckconverterforpemelectrolyzer |