Adaptive Neural Network Nonsingular Fast Terminal Sliding Mode Control for Permanent Magnet Linear Synchronous Motor
For the problem that the position tracking accuracy of permanent magnet linear synchronous motor (PMLSM) servo system is easily affected by uncertain factors such as parameters change, load disturbance and friction and so on, an adaptive neural network nonsingular fast terminal sliding mode control...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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IEEE
2019-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/8930465/ |
| _version_ | 1831560250209075200 |
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| author | Ximei Zhao Dongxue Fu |
| author_facet | Ximei Zhao Dongxue Fu |
| author_sort | Ximei Zhao |
| collection | DOAJ |
| description | For the problem that the position tracking accuracy of permanent magnet linear synchronous motor (PMLSM) servo system is easily affected by uncertain factors such as parameters change, load disturbance and friction and so on, an adaptive neural network nonsingular fast terminal sliding mode control (ANNNFTSMC) method is proposed. Firstly, the PMLSM dynamic mathematical model with uncertainty is established. Then, the nonsingular fast terminal sliding mode control (NFTSMC) can avoid the singularity problem and make the state of the system converge to the equilibrium point quickly, so as to improve the response speed of the system. Secondly, in order to minimize the influence of disturbance and dynamic uncertainty, the dynamic model of PMLSM servo system is estimated by RBF neural network, and the uncertain upper bound of PMLSM servo system is estimated in real time combined with adaptive control, which weakens the chattering phenomenon and enhances the robustness of the system. It is proved theoretically that the control scheme can make the system achieve fast convergence and good tracking. Finally, the system experiments show that the proposed control scheme has the advantages of high tracking accuracy, good robustness, fast response speed and small position error. |
| first_indexed | 2024-12-17T05:42:52Z |
| format | Article |
| id | doaj.art-84403bd53c9c4d6a9c89ab019c5e8fbe |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-17T05:42:52Z |
| publishDate | 2019-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-84403bd53c9c4d6a9c89ab019c5e8fbe2022-12-21T22:01:23ZengIEEEIEEE Access2169-35362019-01-01718036118037210.1109/ACCESS.2019.29585698930465Adaptive Neural Network Nonsingular Fast Terminal Sliding Mode Control for Permanent Magnet Linear Synchronous MotorXimei Zhao0https://orcid.org/0000-0003-4087-2308Dongxue Fu1https://orcid.org/0000-0003-2525-5555School of Electrical Engineering, Shenyang University of Technology, Shenyang, ChinaSchool of Electrical Engineering, Shenyang University of Technology, Shenyang, ChinaFor the problem that the position tracking accuracy of permanent magnet linear synchronous motor (PMLSM) servo system is easily affected by uncertain factors such as parameters change, load disturbance and friction and so on, an adaptive neural network nonsingular fast terminal sliding mode control (ANNNFTSMC) method is proposed. Firstly, the PMLSM dynamic mathematical model with uncertainty is established. Then, the nonsingular fast terminal sliding mode control (NFTSMC) can avoid the singularity problem and make the state of the system converge to the equilibrium point quickly, so as to improve the response speed of the system. Secondly, in order to minimize the influence of disturbance and dynamic uncertainty, the dynamic model of PMLSM servo system is estimated by RBF neural network, and the uncertain upper bound of PMLSM servo system is estimated in real time combined with adaptive control, which weakens the chattering phenomenon and enhances the robustness of the system. It is proved theoretically that the control scheme can make the system achieve fast convergence and good tracking. Finally, the system experiments show that the proposed control scheme has the advantages of high tracking accuracy, good robustness, fast response speed and small position error.https://ieeexplore.ieee.org/document/8930465/Permanent magnet linear synchronous motornonsingular fast terminal sliding mode controladaptiveRBF neural network |
| spellingShingle | Ximei Zhao Dongxue Fu Adaptive Neural Network Nonsingular Fast Terminal Sliding Mode Control for Permanent Magnet Linear Synchronous Motor IEEE Access Permanent magnet linear synchronous motor nonsingular fast terminal sliding mode control adaptive RBF neural network |
| title | Adaptive Neural Network Nonsingular Fast Terminal Sliding Mode Control for Permanent Magnet Linear Synchronous Motor |
| title_full | Adaptive Neural Network Nonsingular Fast Terminal Sliding Mode Control for Permanent Magnet Linear Synchronous Motor |
| title_fullStr | Adaptive Neural Network Nonsingular Fast Terminal Sliding Mode Control for Permanent Magnet Linear Synchronous Motor |
| title_full_unstemmed | Adaptive Neural Network Nonsingular Fast Terminal Sliding Mode Control for Permanent Magnet Linear Synchronous Motor |
| title_short | Adaptive Neural Network Nonsingular Fast Terminal Sliding Mode Control for Permanent Magnet Linear Synchronous Motor |
| title_sort | adaptive neural network nonsingular fast terminal sliding mode control for permanent magnet linear synchronous motor |
| topic | Permanent magnet linear synchronous motor nonsingular fast terminal sliding mode control adaptive RBF neural network |
| url | https://ieeexplore.ieee.org/document/8930465/ |
| work_keys_str_mv | AT ximeizhao adaptiveneuralnetworknonsingularfastterminalslidingmodecontrolforpermanentmagnetlinearsynchronousmotor AT dongxuefu adaptiveneuralnetworknonsingularfastterminalslidingmodecontrolforpermanentmagnetlinearsynchronousmotor |