Self-Calibration of Angular Position Sensors by Signal Flow Networks
Angle position sensors (APSs) usually require initial calibration to improve their accuracy. This article introduces a novel offline self-calibration scheme in which a signal flow network is employed to reduce the amplitude errors, direct-current (DC) offsets, and phase shift without requiring extra...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2018-08-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | http://www.mdpi.com/1424-8220/18/8/2513 |
| _version_ | 1798002719251759104 |
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| author | Zhenyi Gao Bin Zhou Bo Hou Chao Li Qi Wei Rong Zhang |
| author_facet | Zhenyi Gao Bin Zhou Bo Hou Chao Li Qi Wei Rong Zhang |
| author_sort | Zhenyi Gao |
| collection | DOAJ |
| description | Angle position sensors (APSs) usually require initial calibration to improve their accuracy. This article introduces a novel offline self-calibration scheme in which a signal flow network is employed to reduce the amplitude errors, direct-current (DC) offsets, and phase shift without requiring extra calibration instruments. In this approach, a signal flow network is firstly constructed to overcome the parametric coupling caused by the linearization model and to ensure the independence of the parameters. The model parameters are stored in the nodes of the network, and the intermediate variables are input into the optimization pipeline to overcome the local optimization problem. A deep learning algorithm is also used to improve the accuracy and speed of convergence to a global optimal solution. The results of simulations show that the proposed method can achieve a high identification accuracy with a relative parameter identification error less than 0.001‰. The practical effects were also verified by implementing the developed technique in a capacitive APS, and the experimental results demonstrate that the sensor error after signal calibration could be reduced to only 6.98%. |
| first_indexed | 2024-04-11T11:56:46Z |
| format | Article |
| id | doaj.art-9df6afe9be8640119eac010bb2687097 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-11T11:56:46Z |
| publishDate | 2018-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-9df6afe9be8640119eac010bb26870972022-12-22T04:25:07ZengMDPI AGSensors1424-82202018-08-01188251310.3390/s18082513s18082513Self-Calibration of Angular Position Sensors by Signal Flow NetworksZhenyi Gao0Bin Zhou1Bo Hou2Chao Li3Qi Wei4Rong Zhang5Engineering Research Center for Navigation Technology, Department of Precision Instrument, Tsinghua University, Beijing 100084, ChinaEngineering Research Center for Navigation Technology, Department of Precision Instrument, Tsinghua University, Beijing 100084, ChinaEngineering Research Center for Navigation Technology, Department of Precision Instrument, Tsinghua University, Beijing 100084, ChinaEngineering Research Center for Navigation Technology, Department of Precision Instrument, Tsinghua University, Beijing 100084, ChinaDepartment of Electronic Engineering, Tsinghua University, Beijing 100084, ChinaEngineering Research Center for Navigation Technology, Department of Precision Instrument, Tsinghua University, Beijing 100084, ChinaAngle position sensors (APSs) usually require initial calibration to improve their accuracy. This article introduces a novel offline self-calibration scheme in which a signal flow network is employed to reduce the amplitude errors, direct-current (DC) offsets, and phase shift without requiring extra calibration instruments. In this approach, a signal flow network is firstly constructed to overcome the parametric coupling caused by the linearization model and to ensure the independence of the parameters. The model parameters are stored in the nodes of the network, and the intermediate variables are input into the optimization pipeline to overcome the local optimization problem. A deep learning algorithm is also used to improve the accuracy and speed of convergence to a global optimal solution. The results of simulations show that the proposed method can achieve a high identification accuracy with a relative parameter identification error less than 0.001‰. The practical effects were also verified by implementing the developed technique in a capacitive APS, and the experimental results demonstrate that the sensor error after signal calibration could be reduced to only 6.98%.http://www.mdpi.com/1424-8220/18/8/2513self-calibrationsignal flow networksignal processingangular position sensor |
| spellingShingle | Zhenyi Gao Bin Zhou Bo Hou Chao Li Qi Wei Rong Zhang Self-Calibration of Angular Position Sensors by Signal Flow Networks Sensors self-calibration signal flow network signal processing angular position sensor |
| title | Self-Calibration of Angular Position Sensors by Signal Flow Networks |
| title_full | Self-Calibration of Angular Position Sensors by Signal Flow Networks |
| title_fullStr | Self-Calibration of Angular Position Sensors by Signal Flow Networks |
| title_full_unstemmed | Self-Calibration of Angular Position Sensors by Signal Flow Networks |
| title_short | Self-Calibration of Angular Position Sensors by Signal Flow Networks |
| title_sort | self calibration of angular position sensors by signal flow networks |
| topic | self-calibration signal flow network signal processing angular position sensor |
| url | http://www.mdpi.com/1424-8220/18/8/2513 |
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