Adaptive Fractional Prescribed Performance Control for Micro-Electromechanical System Gyros Using a Modified Neural Estimator
In this paper, a neural fractional order prescribed performance control is proposed for micro-electromechanical system (MEMS) gyros. Gyros tend to become smaller in size and more precise in structure with the development of micro-manufacturing technology. The operational security for MEMS gyros in c...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-10-01
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| Series: | Electronics |
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| Online Access: | https://www.mdpi.com/2079-9292/12/21/4409 |
| _version_ | 1797632013978566656 |
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| author | Cheng Lu Zhiwei Wen Laiwu Luo Yunxiang Guo Xinsong Zhang |
| author_facet | Cheng Lu Zhiwei Wen Laiwu Luo Yunxiang Guo Xinsong Zhang |
| author_sort | Cheng Lu |
| collection | DOAJ |
| description | In this paper, a neural fractional order prescribed performance control is proposed for micro-electromechanical system (MEMS) gyros. Gyros tend to become smaller in size and more precise in structure with the development of micro-manufacturing technology. The operational security for MEMS gyros in cases of disturbances and parameter uncertainties becomes a challenging problem that has attracted much attention. The proposed method incorporates a prescribed performance technique to accomplish a bounded (within 10% of the vibration amplitude) gyro trajectory tracking error dynamic to secure the gyro’s operation. Meanwhile, fractional calculus is integrated into the controller’s design to provide precise adjustments to the gyro’s motion and thus further improve gyro control performance (shortening error convergence time by 20%). Furthermore, to enlarge the application scope and to improve gyro system robustness, a modified neural network estimator with a constrained input mapping mechanism is proposed to approximate unknown time-varying angular-velocity-related gyro dynamics. Notably, the constrained input mapping mechanism can help guide neural parameter initialization to avoid a time-consuming parameter adjustment process. The stability of the closed-loop gyro control system is proved in the framework of Lyapunov stability theory, and comparisons of simulation results are presented to demonstrate the effectiveness of the proposed method. |
| first_indexed | 2024-03-11T11:31:52Z |
| format | Article |
| id | doaj.art-99336bc9e0884c308b01643cade2abdd |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-11T11:31:52Z |
| publishDate | 2023-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-99336bc9e0884c308b01643cade2abdd2023-11-10T15:01:19ZengMDPI AGElectronics2079-92922023-10-011221440910.3390/electronics12214409Adaptive Fractional Prescribed Performance Control for Micro-Electromechanical System Gyros Using a Modified Neural EstimatorCheng Lu0Zhiwei Wen1Laiwu Luo2Yunxiang Guo3Xinsong Zhang4College of Electrical Engineering, Nantong University, Nantong 226019, ChinaCollege of Electrical Engineering, Nantong University, Nantong 226019, ChinaCollege of Electrical Engineering, Nantong University, Nantong 226019, ChinaCollege of Electrical Engineering, Nantong University, Nantong 226019, ChinaCollege of Electrical Engineering, Nantong University, Nantong 226019, ChinaIn this paper, a neural fractional order prescribed performance control is proposed for micro-electromechanical system (MEMS) gyros. Gyros tend to become smaller in size and more precise in structure with the development of micro-manufacturing technology. The operational security for MEMS gyros in cases of disturbances and parameter uncertainties becomes a challenging problem that has attracted much attention. The proposed method incorporates a prescribed performance technique to accomplish a bounded (within 10% of the vibration amplitude) gyro trajectory tracking error dynamic to secure the gyro’s operation. Meanwhile, fractional calculus is integrated into the controller’s design to provide precise adjustments to the gyro’s motion and thus further improve gyro control performance (shortening error convergence time by 20%). Furthermore, to enlarge the application scope and to improve gyro system robustness, a modified neural network estimator with a constrained input mapping mechanism is proposed to approximate unknown time-varying angular-velocity-related gyro dynamics. Notably, the constrained input mapping mechanism can help guide neural parameter initialization to avoid a time-consuming parameter adjustment process. The stability of the closed-loop gyro control system is proved in the framework of Lyapunov stability theory, and comparisons of simulation results are presented to demonstrate the effectiveness of the proposed method.https://www.mdpi.com/2079-9292/12/21/4409adaptive controlneural networkgyroscope |
| spellingShingle | Cheng Lu Zhiwei Wen Laiwu Luo Yunxiang Guo Xinsong Zhang Adaptive Fractional Prescribed Performance Control for Micro-Electromechanical System Gyros Using a Modified Neural Estimator Electronics adaptive control neural network gyroscope |
| title | Adaptive Fractional Prescribed Performance Control for Micro-Electromechanical System Gyros Using a Modified Neural Estimator |
| title_full | Adaptive Fractional Prescribed Performance Control for Micro-Electromechanical System Gyros Using a Modified Neural Estimator |
| title_fullStr | Adaptive Fractional Prescribed Performance Control for Micro-Electromechanical System Gyros Using a Modified Neural Estimator |
| title_full_unstemmed | Adaptive Fractional Prescribed Performance Control for Micro-Electromechanical System Gyros Using a Modified Neural Estimator |
| title_short | Adaptive Fractional Prescribed Performance Control for Micro-Electromechanical System Gyros Using a Modified Neural Estimator |
| title_sort | adaptive fractional prescribed performance control for micro electromechanical system gyros using a modified neural estimator |
| topic | adaptive control neural network gyroscope |
| url | https://www.mdpi.com/2079-9292/12/21/4409 |
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