Minimal-Learning-Parameter Technique Based Adaptive Neural Sliding Mode Control of MEMS Gyroscope
This paper investigates an adaptive neural sliding mode controller for MEMS gyroscopes with minimal-learning-parameter technique. Considering the system uncertainty in dynamics, neural network is employed for approximation. Minimal-learning-parameter technique is constructed to decrease the number o...
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| Format: | Article |
| Language: | English |
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Wiley
2017-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2017/6019175 |
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| _version_ | 1850238023253360640 |
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| author | Bin Xu Pengchao Zhang |
| author_facet | Bin Xu Pengchao Zhang |
| author_sort | Bin Xu |
| collection | DOAJ |
| description | This paper investigates an adaptive neural sliding mode controller for MEMS gyroscopes with minimal-learning-parameter technique. Considering the system uncertainty in dynamics, neural network is employed for approximation. Minimal-learning-parameter technique is constructed to decrease the number of update parameters, and in this way the computation burden is greatly reduced. Sliding mode control is designed to cancel the effect of time-varying disturbance. The closed-loop stability analysis is established via Lyapunov approach. Simulation results are presented to demonstrate the effectiveness of the method. |
| format | Article |
| id | doaj-art-07b99265dbf646d0a97e04f92cc7f069 |
| institution | OA Journals |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2017-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-07b99265dbf646d0a97e04f92cc7f0692025-08-20T02:01:35ZengWileyComplexity1076-27871099-05262017-01-01201710.1155/2017/60191756019175Minimal-Learning-Parameter Technique Based Adaptive Neural Sliding Mode Control of MEMS GyroscopeBin Xu0Pengchao Zhang1Shaanxi Provincial Key Laboratory of Industrial Automation, Shaanxi University of Technology, Hanzhong, Shaanxi 723000, ChinaShaanxi Provincial Key Laboratory of Industrial Automation, Shaanxi University of Technology, Hanzhong, Shaanxi 723000, ChinaThis paper investigates an adaptive neural sliding mode controller for MEMS gyroscopes with minimal-learning-parameter technique. Considering the system uncertainty in dynamics, neural network is employed for approximation. Minimal-learning-parameter technique is constructed to decrease the number of update parameters, and in this way the computation burden is greatly reduced. Sliding mode control is designed to cancel the effect of time-varying disturbance. The closed-loop stability analysis is established via Lyapunov approach. Simulation results are presented to demonstrate the effectiveness of the method.http://dx.doi.org/10.1155/2017/6019175 |
| spellingShingle | Bin Xu Pengchao Zhang Minimal-Learning-Parameter Technique Based Adaptive Neural Sliding Mode Control of MEMS Gyroscope Complexity |
| title | Minimal-Learning-Parameter Technique Based Adaptive Neural Sliding Mode Control of MEMS Gyroscope |
| title_full | Minimal-Learning-Parameter Technique Based Adaptive Neural Sliding Mode Control of MEMS Gyroscope |
| title_fullStr | Minimal-Learning-Parameter Technique Based Adaptive Neural Sliding Mode Control of MEMS Gyroscope |
| title_full_unstemmed | Minimal-Learning-Parameter Technique Based Adaptive Neural Sliding Mode Control of MEMS Gyroscope |
| title_short | Minimal-Learning-Parameter Technique Based Adaptive Neural Sliding Mode Control of MEMS Gyroscope |
| title_sort | minimal learning parameter technique based adaptive neural sliding mode control of mems gyroscope |
| url | http://dx.doi.org/10.1155/2017/6019175 |
| work_keys_str_mv | AT binxu minimallearningparametertechniquebasedadaptiveneuralslidingmodecontrolofmemsgyroscope AT pengchaozhang minimallearningparametertechniquebasedadaptiveneuralslidingmodecontrolofmemsgyroscope |