SAW Torque Sensor Gyroscopic Effect Compensation by Least Squares Support Vector Machine Algorithm Based on Chaos Estimation of Distributed Algorithm
As this study examined the issue of surface acoustic wave (SAW) torque sensor which interfered in high rotational speed, the gyroscopic effect generated by rotation was analyzed. Firstly, the SAW coupled equations which contained torque and rotation loads were deduced, and the torque calculation err...
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| Format: | Article |
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MDPI AG
2019-06-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/19/12/2768 |
| _version_ | 1798003452701310976 |
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| author | Wei Han Xiongzhu Bu Yihan Cao Miaomiao Xu |
| author_facet | Wei Han Xiongzhu Bu Yihan Cao Miaomiao Xu |
| author_sort | Wei Han |
| collection | DOAJ |
| description | As this study examined the issue of surface acoustic wave (SAW) torque sensor which interfered in high rotational speed, the gyroscopic effect generated by rotation was analyzed. Firstly, the SAW coupled equations which contained torque and rotation loads were deduced, and the torque calculation error caused by rotation was solved. Following this, the hardware of the SAW gyroscopic effect testing platform and the turntable experiment were designed to verify the correctness of the theoretical calculation. Finally, according to the experimental data, the gyroscopic effect was compensated by multivariate polynomial fitting (MPF), Gaussian processes regression (GPR), and least squares support vector machine algorithms (LSSVM). The comparison results showed that the LSSVM has the obvious advantage. For improving the function of LSSVM model, chaos estimation of distributed algorithm (CEDA) was proposed to optimize the super parameters of the LSSVM, and numerical simulation results showed that: (1) CEDA is superior to traditional estimation of distributed algorithms in convergence speed and anti-premature ability; (2) the performance of CEDA-LSSVM is better than genetic algorithms (GA)-LSSVM and particle swarm optimization (PSO)-LSSVM. After compensating by CEDA-LSSVM, the magnitude of the torque calculation relative error was 10<sup>−4</sup> in any direction. This method has a significant effect on reducing gyroscopic interference, and it lays a foundation for the engineering application of SAW torque sensor. |
| first_indexed | 2024-04-11T12:07:51Z |
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| id | doaj.art-e3454dd489ed49e3a4364b85f888df06 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-11T12:07:51Z |
| publishDate | 2019-06-01 |
| publisher | MDPI AG |
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| series | Sensors |
| spelling | doaj.art-e3454dd489ed49e3a4364b85f888df062022-12-22T04:24:41ZengMDPI AGSensors1424-82202019-06-011912276810.3390/s19122768s19122768SAW Torque Sensor Gyroscopic Effect Compensation by Least Squares Support Vector Machine Algorithm Based on Chaos Estimation of Distributed AlgorithmWei Han0Xiongzhu Bu1Yihan Cao2Miaomiao Xu3Nanjing University of Science and Technology, School of Mechanical Engineering, 200 Xiaolingwei, 210094 Nanjing, ChinaNanjing University of Science and Technology, School of Mechanical Engineering, 200 Xiaolingwei, 210094 Nanjing, ChinaNanjing University of Science and Technology, School of Mechanical Engineering, 200 Xiaolingwei, 210094 Nanjing, ChinaNanjing University of Science and Technology, School of Mechanical Engineering, 200 Xiaolingwei, 210094 Nanjing, ChinaAs this study examined the issue of surface acoustic wave (SAW) torque sensor which interfered in high rotational speed, the gyroscopic effect generated by rotation was analyzed. Firstly, the SAW coupled equations which contained torque and rotation loads were deduced, and the torque calculation error caused by rotation was solved. Following this, the hardware of the SAW gyroscopic effect testing platform and the turntable experiment were designed to verify the correctness of the theoretical calculation. Finally, according to the experimental data, the gyroscopic effect was compensated by multivariate polynomial fitting (MPF), Gaussian processes regression (GPR), and least squares support vector machine algorithms (LSSVM). The comparison results showed that the LSSVM has the obvious advantage. For improving the function of LSSVM model, chaos estimation of distributed algorithm (CEDA) was proposed to optimize the super parameters of the LSSVM, and numerical simulation results showed that: (1) CEDA is superior to traditional estimation of distributed algorithms in convergence speed and anti-premature ability; (2) the performance of CEDA-LSSVM is better than genetic algorithms (GA)-LSSVM and particle swarm optimization (PSO)-LSSVM. After compensating by CEDA-LSSVM, the magnitude of the torque calculation relative error was 10<sup>−4</sup> in any direction. This method has a significant effect on reducing gyroscopic interference, and it lays a foundation for the engineering application of SAW torque sensor.https://www.mdpi.com/1424-8220/19/12/2768SAWtorque sensorgyroscopic effect compensationCEDA-LSSVM |
| spellingShingle | Wei Han Xiongzhu Bu Yihan Cao Miaomiao Xu SAW Torque Sensor Gyroscopic Effect Compensation by Least Squares Support Vector Machine Algorithm Based on Chaos Estimation of Distributed Algorithm Sensors SAW torque sensor gyroscopic effect compensation CEDA-LSSVM |
| title | SAW Torque Sensor Gyroscopic Effect Compensation by Least Squares Support Vector Machine Algorithm Based on Chaos Estimation of Distributed Algorithm |
| title_full | SAW Torque Sensor Gyroscopic Effect Compensation by Least Squares Support Vector Machine Algorithm Based on Chaos Estimation of Distributed Algorithm |
| title_fullStr | SAW Torque Sensor Gyroscopic Effect Compensation by Least Squares Support Vector Machine Algorithm Based on Chaos Estimation of Distributed Algorithm |
| title_full_unstemmed | SAW Torque Sensor Gyroscopic Effect Compensation by Least Squares Support Vector Machine Algorithm Based on Chaos Estimation of Distributed Algorithm |
| title_short | SAW Torque Sensor Gyroscopic Effect Compensation by Least Squares Support Vector Machine Algorithm Based on Chaos Estimation of Distributed Algorithm |
| title_sort | saw torque sensor gyroscopic effect compensation by least squares support vector machine algorithm based on chaos estimation of distributed algorithm |
| topic | SAW torque sensor gyroscopic effect compensation CEDA-LSSVM |
| url | https://www.mdpi.com/1424-8220/19/12/2768 |
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