Adaptive Neural Sliding Mode Control of Active Power Filter
A radial basis function (RBF) neural network adaptive sliding mode control system is developed for the current compensation control of three-phase active power filter (APF). The advantages of the adaptive control, neural network control, and sliding mode control are combined together to achieve the...
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| Format: | Article |
| Language: | English |
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Wiley
2013-01-01
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| Series: | Journal of Applied Mathematics |
| Online Access: | http://dx.doi.org/10.1155/2013/341831 |
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| _version_ | 1832553755492483072 |
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| author | Juntao Fei Zhe Wang |
| author_facet | Juntao Fei Zhe Wang |
| author_sort | Juntao Fei |
| collection | DOAJ |
| description | A radial basis function (RBF) neural network adaptive sliding mode control system is developed for the current compensation control of three-phase active power filter (APF). The advantages of the adaptive control, neural network control, and sliding mode control are combined together to achieve the control task; that is, the harmonic current of nonlinear load can be eliminated and the quality of power system can be well improved. Sliding surface coordinate function and sliding mode controller are used as input and output of the RBF neural network, respectively. The neural network control parameters are online adjusted through gradient method and Lyapunov theory. Simulation results demonstrate that the adaptive RBF sliding mode control can compensate harmonic current effectively and has strong robustness to disturbance signals. |
| format | Article |
| id | doaj-art-6fe604fa0f3d495d97721df25f8b066f |
| institution | Kabale University |
| issn | 1110-757X 1687-0042 |
| language | English |
| publishDate | 2013-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Applied Mathematics |
| spelling | doaj-art-6fe604fa0f3d495d97721df25f8b066f2025-02-03T05:53:18ZengWileyJournal of Applied Mathematics1110-757X1687-00422013-01-01201310.1155/2013/341831341831Adaptive Neural Sliding Mode Control of Active Power FilterJuntao Fei0Zhe Wang1Jiangsu Key Laboratory of Power Transmission and Distribution Equipment Technology, College of Computer and Information, Hohai University, Changzhou 213022, ChinaJiangsu Key Laboratory of Power Transmission and Distribution Equipment Technology, College of Computer and Information, Hohai University, Changzhou 213022, ChinaA radial basis function (RBF) neural network adaptive sliding mode control system is developed for the current compensation control of three-phase active power filter (APF). The advantages of the adaptive control, neural network control, and sliding mode control are combined together to achieve the control task; that is, the harmonic current of nonlinear load can be eliminated and the quality of power system can be well improved. Sliding surface coordinate function and sliding mode controller are used as input and output of the RBF neural network, respectively. The neural network control parameters are online adjusted through gradient method and Lyapunov theory. Simulation results demonstrate that the adaptive RBF sliding mode control can compensate harmonic current effectively and has strong robustness to disturbance signals.http://dx.doi.org/10.1155/2013/341831 |
| spellingShingle | Juntao Fei Zhe Wang Adaptive Neural Sliding Mode Control of Active Power Filter Journal of Applied Mathematics |
| title | Adaptive Neural Sliding Mode Control of Active Power Filter |
| title_full | Adaptive Neural Sliding Mode Control of Active Power Filter |
| title_fullStr | Adaptive Neural Sliding Mode Control of Active Power Filter |
| title_full_unstemmed | Adaptive Neural Sliding Mode Control of Active Power Filter |
| title_short | Adaptive Neural Sliding Mode Control of Active Power Filter |
| title_sort | adaptive neural sliding mode control of active power filter |
| url | http://dx.doi.org/10.1155/2013/341831 |
| work_keys_str_mv | AT juntaofei adaptiveneuralslidingmodecontrolofactivepowerfilter AT zhewang adaptiveneuralslidingmodecontrolofactivepowerfilter |