A Hybrid Adaptive Controller Applied for Oscillating System
In this paper, a hybrid PI radial basis function neural network (RBFNN) controller is used for a plant with significant disturbances related to the mechanical part of the construction. It is represented through a two-mass system. State variables contain additional components—as a result, oscillation...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-08-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/15/17/6265 |
| _version_ | 1797495637201125376 |
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| author | Radoslaw Stanislawski Jules-Raymond Tapamo Marcin Kaminski |
| author_facet | Radoslaw Stanislawski Jules-Raymond Tapamo Marcin Kaminski |
| author_sort | Radoslaw Stanislawski |
| collection | DOAJ |
| description | In this paper, a hybrid PI radial basis function neural network (RBFNN) controller is used for a plant with significant disturbances related to the mechanical part of the construction. It is represented through a two-mass system. State variables contain additional components—as a result, oscillations affect the precision of control. Classical solutions lead to movements of the poles of the whole control structure. However, proper tuning of the controller needs detailed identification of the object. In this work, the neural network is implemented to improve the classical PI controller’s performance and mitigate the errors generated by oscillations of the mechanical variables and parametric uncertainties. The proposed control strategy also guarantees the closed-loop stability of the system. The mathematical background is firstly presented. Afterward, the simulation results are shown. It can be stated that the results are very promising, and a significant improvement in oscillations damping is achieved. Finally, experimental tests are conducted to substantiate the obtained simulation results. For this purpose, the algorithm was implemented in the dSPACE card. Achieved transients confirm the numerical tests. |
| first_indexed | 2024-03-10T01:52:29Z |
| format | Article |
| id | doaj.art-f592c8ada9d94249aec71ffa3f494998 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-10T01:52:29Z |
| publishDate | 2022-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-f592c8ada9d94249aec71ffa3f4949982023-11-23T13:03:11ZengMDPI AGEnergies1996-10732022-08-011517626510.3390/en15176265A Hybrid Adaptive Controller Applied for Oscillating SystemRadoslaw Stanislawski0Jules-Raymond Tapamo1Marcin Kaminski2Department of Electrical Machines, Drives and Measurements, Faculty of Electrical Engineering, Wroclaw University of Science and Technology, 50-372 Wroclaw, PolandSchool of Engineering, University of KwaZulu-Natal, Durban 4041, South AfricaDepartment of Electrical Machines, Drives and Measurements, Faculty of Electrical Engineering, Wroclaw University of Science and Technology, 50-372 Wroclaw, PolandIn this paper, a hybrid PI radial basis function neural network (RBFNN) controller is used for a plant with significant disturbances related to the mechanical part of the construction. It is represented through a two-mass system. State variables contain additional components—as a result, oscillations affect the precision of control. Classical solutions lead to movements of the poles of the whole control structure. However, proper tuning of the controller needs detailed identification of the object. In this work, the neural network is implemented to improve the classical PI controller’s performance and mitigate the errors generated by oscillations of the mechanical variables and parametric uncertainties. The proposed control strategy also guarantees the closed-loop stability of the system. The mathematical background is firstly presented. Afterward, the simulation results are shown. It can be stated that the results are very promising, and a significant improvement in oscillations damping is achieved. Finally, experimental tests are conducted to substantiate the obtained simulation results. For this purpose, the algorithm was implemented in the dSPACE card. Achieved transients confirm the numerical tests.https://www.mdpi.com/1996-1073/15/17/6265hybrid controlleradaptive controlradial basis function neural networkoscillating systemstwo-mass systemvibration suppression |
| spellingShingle | Radoslaw Stanislawski Jules-Raymond Tapamo Marcin Kaminski A Hybrid Adaptive Controller Applied for Oscillating System Energies hybrid controller adaptive control radial basis function neural network oscillating systems two-mass system vibration suppression |
| title | A Hybrid Adaptive Controller Applied for Oscillating System |
| title_full | A Hybrid Adaptive Controller Applied for Oscillating System |
| title_fullStr | A Hybrid Adaptive Controller Applied for Oscillating System |
| title_full_unstemmed | A Hybrid Adaptive Controller Applied for Oscillating System |
| title_short | A Hybrid Adaptive Controller Applied for Oscillating System |
| title_sort | hybrid adaptive controller applied for oscillating system |
| topic | hybrid controller adaptive control radial basis function neural network oscillating systems two-mass system vibration suppression |
| url | https://www.mdpi.com/1996-1073/15/17/6265 |
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