Novel Fuzzy Neural Nonsingular Terminal Sliding Mode Control of MEMS Gyroscope
This paper attempts to improve the robustness and rapidity of a microgyroscope sensor by presenting a double-loop recurrent fuzzy neural network based on a nonsingular terminal sliding mode controller. Compared with the traditional control method, the proposed strategy can obtain faster dynamic resp...
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| Format: | Article |
| Language: | English |
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Wiley
2019-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2019/6840639 |
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| _version_ | 1832548660498399232 |
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| author | Zhe Wang Juntao Fei |
| author_facet | Zhe Wang Juntao Fei |
| author_sort | Zhe Wang |
| collection | DOAJ |
| description | This paper attempts to improve the robustness and rapidity of a microgyroscope sensor by presenting a double-loop recurrent fuzzy neural network based on a nonsingular terminal sliding mode controller. Compared with the traditional control method, the proposed strategy can obtain faster dynamic response speed and lower steady-state error with high robustness in the presence of system uncertainties and external disturbances. A nonlinear terminal sliding mode controller is designed to guarantee finite-time high-precision convergence of the sliding surface and meanwhile to eliminate the effect of singularity. Moreover, an exponential approach law is used to accelerate the convergence rate of the system to the sliding surface. For suppressing the chattering, the symbolic function in the ideal sliding mode is replaced by the saturation function. To suppress the effect of model uncertainties and external disturbances, a double-loop recurrent fuzzy neural network is introduced to approximate and compensate system nonlinearities for the gyroscope sensor. At the same time, the double-loop recurrent fuzzy neural network can effectively accelerate the speed of parameter learning by introducing the adaptive mechanism. Simulation results indicate that the control system with the proposed controller is easily implemented, and it has higher tracking precision and considerable robustness to model uncertainties compared with the existing controllers. |
| format | Article |
| id | doaj-art-227c01cd1f5d4fbd91f77dc27f1bdcb5 |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2019-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-227c01cd1f5d4fbd91f77dc27f1bdcb52025-02-03T06:13:26ZengWileyComplexity1076-27871099-05262019-01-01201910.1155/2019/68406396840639Novel Fuzzy Neural Nonsingular Terminal Sliding Mode Control of MEMS GyroscopeZhe Wang0Juntao Fei1College of IoT Engineering, Hohai University, Changzhou 213022, ChinaJiangsu Key Lab of Power Transmission and Distribution Equipment Technology, Changzhou, ChinaThis paper attempts to improve the robustness and rapidity of a microgyroscope sensor by presenting a double-loop recurrent fuzzy neural network based on a nonsingular terminal sliding mode controller. Compared with the traditional control method, the proposed strategy can obtain faster dynamic response speed and lower steady-state error with high robustness in the presence of system uncertainties and external disturbances. A nonlinear terminal sliding mode controller is designed to guarantee finite-time high-precision convergence of the sliding surface and meanwhile to eliminate the effect of singularity. Moreover, an exponential approach law is used to accelerate the convergence rate of the system to the sliding surface. For suppressing the chattering, the symbolic function in the ideal sliding mode is replaced by the saturation function. To suppress the effect of model uncertainties and external disturbances, a double-loop recurrent fuzzy neural network is introduced to approximate and compensate system nonlinearities for the gyroscope sensor. At the same time, the double-loop recurrent fuzzy neural network can effectively accelerate the speed of parameter learning by introducing the adaptive mechanism. Simulation results indicate that the control system with the proposed controller is easily implemented, and it has higher tracking precision and considerable robustness to model uncertainties compared with the existing controllers.http://dx.doi.org/10.1155/2019/6840639 |
| spellingShingle | Zhe Wang Juntao Fei Novel Fuzzy Neural Nonsingular Terminal Sliding Mode Control of MEMS Gyroscope Complexity |
| title | Novel Fuzzy Neural Nonsingular Terminal Sliding Mode Control of MEMS Gyroscope |
| title_full | Novel Fuzzy Neural Nonsingular Terminal Sliding Mode Control of MEMS Gyroscope |
| title_fullStr | Novel Fuzzy Neural Nonsingular Terminal Sliding Mode Control of MEMS Gyroscope |
| title_full_unstemmed | Novel Fuzzy Neural Nonsingular Terminal Sliding Mode Control of MEMS Gyroscope |
| title_short | Novel Fuzzy Neural Nonsingular Terminal Sliding Mode Control of MEMS Gyroscope |
| title_sort | novel fuzzy neural nonsingular terminal sliding mode control of mems gyroscope |
| url | http://dx.doi.org/10.1155/2019/6840639 |
| work_keys_str_mv | AT zhewang novelfuzzyneuralnonsingularterminalslidingmodecontrolofmemsgyroscope AT juntaofei novelfuzzyneuralnonsingularterminalslidingmodecontrolofmemsgyroscope |