Nonlinear Control of an Active Magnetic Bearing System Achieved Using a Fuzzy Control with Radial Basis Function Neural Network
Studies on active magnetic bearing (AMB) systems are increasing in popularity and practical applications. Magnetic bearings cause less noise, friction, and vibration than the conventional mechanical bearings; however, the control of AMB systems requires further investigation. The magnetic force has...
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| Format: | Article |
| Language: | English |
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Wiley
2014-01-01
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| Series: | Journal of Applied Mathematics |
| Online Access: | http://dx.doi.org/10.1155/2014/272391 |
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| author | Seng-Chi Chen Van-Sum Nguyen Dinh-Kha Le Nguyen Thi Hoai Nam |
| author_facet | Seng-Chi Chen Van-Sum Nguyen Dinh-Kha Le Nguyen Thi Hoai Nam |
| author_sort | Seng-Chi Chen |
| collection | DOAJ |
| description | Studies on active magnetic bearing (AMB) systems are increasing in popularity and practical applications. Magnetic bearings cause less noise, friction, and vibration than the conventional mechanical bearings; however, the control of AMB systems requires further investigation. The magnetic force has a highly nonlinear relation to the control current and the air gap. This paper proposes an intelligent control method for positioning an AMB system that uses a neural fuzzy controller (NFC). The mathematical model of an AMB system comprises identification followed by collection of information from this system. A fuzzy logic controller (FLC), the parameters of which are adjusted using a radial basis function neural network (RBFNN), is applied to the unbalanced vibration in an AMB system. The AMB system exhibited a satisfactory control performance, with low overshoot, and produced improved transient and steady-state responses under various operating conditions. The NFC has been verified on a prototype AMB system. The proposed controller can be feasibly applied to AMB systems exposed to various external disturbances; demonstrating the effectiveness of the NFC with self-learning and self-improving capacities is proven. |
| format | Article |
| id | doaj-art-8d272892b32e4c96942ba82faef4432d |
| institution | Kabale University |
| issn | 1110-757X 1687-0042 |
| language | English |
| publishDate | 2014-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Applied Mathematics |
| spelling | doaj-art-8d272892b32e4c96942ba82faef4432d2025-02-03T01:23:04ZengWileyJournal of Applied Mathematics1110-757X1687-00422014-01-01201410.1155/2014/272391272391Nonlinear Control of an Active Magnetic Bearing System Achieved Using a Fuzzy Control with Radial Basis Function Neural NetworkSeng-Chi Chen0Van-Sum Nguyen1Dinh-Kha Le2Nguyen Thi Hoai Nam3Department of Electrical Engineering, Da-Yeh University, Changhua 51591, TaiwanDepartment of Electrical Engineering, Da-Yeh University, Changhua 51591, TaiwanDepartment of Electrical Engineering, Da-Yeh University, Changhua 51591, TaiwanDepartment of Electrical Engineering, Hue Industrial College, Hue 47000, VietnamStudies on active magnetic bearing (AMB) systems are increasing in popularity and practical applications. Magnetic bearings cause less noise, friction, and vibration than the conventional mechanical bearings; however, the control of AMB systems requires further investigation. The magnetic force has a highly nonlinear relation to the control current and the air gap. This paper proposes an intelligent control method for positioning an AMB system that uses a neural fuzzy controller (NFC). The mathematical model of an AMB system comprises identification followed by collection of information from this system. A fuzzy logic controller (FLC), the parameters of which are adjusted using a radial basis function neural network (RBFNN), is applied to the unbalanced vibration in an AMB system. The AMB system exhibited a satisfactory control performance, with low overshoot, and produced improved transient and steady-state responses under various operating conditions. The NFC has been verified on a prototype AMB system. The proposed controller can be feasibly applied to AMB systems exposed to various external disturbances; demonstrating the effectiveness of the NFC with self-learning and self-improving capacities is proven.http://dx.doi.org/10.1155/2014/272391 |
| spellingShingle | Seng-Chi Chen Van-Sum Nguyen Dinh-Kha Le Nguyen Thi Hoai Nam Nonlinear Control of an Active Magnetic Bearing System Achieved Using a Fuzzy Control with Radial Basis Function Neural Network Journal of Applied Mathematics |
| title | Nonlinear Control of an Active Magnetic Bearing System Achieved Using a Fuzzy Control with Radial Basis Function Neural Network |
| title_full | Nonlinear Control of an Active Magnetic Bearing System Achieved Using a Fuzzy Control with Radial Basis Function Neural Network |
| title_fullStr | Nonlinear Control of an Active Magnetic Bearing System Achieved Using a Fuzzy Control with Radial Basis Function Neural Network |
| title_full_unstemmed | Nonlinear Control of an Active Magnetic Bearing System Achieved Using a Fuzzy Control with Radial Basis Function Neural Network |
| title_short | Nonlinear Control of an Active Magnetic Bearing System Achieved Using a Fuzzy Control with Radial Basis Function Neural Network |
| title_sort | nonlinear control of an active magnetic bearing system achieved using a fuzzy control with radial basis function neural network |
| url | http://dx.doi.org/10.1155/2014/272391 |
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