A Portable Support Attitude Sensing System for Accurate Attitude Estimation of Hydraulic Support Based on Unscented Kalman Filter
To measure the support attitude of hydraulic support, a support attitude sensing system composed of an inertial measurement unit with microelectromechanical system (MEMS) was designed in this study. Yaw angle estimation with magnetometers is disturbed by the perturbed magnetic field generated by coa...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-09-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/20/19/5459 |
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| author | Xuliang Lu Zhongbin Wang Chao Tan Haifeng Yan Lei Si Dong Wei |
| author_facet | Xuliang Lu Zhongbin Wang Chao Tan Haifeng Yan Lei Si Dong Wei |
| author_sort | Xuliang Lu |
| collection | DOAJ |
| description | To measure the support attitude of hydraulic support, a support attitude sensing system composed of an inertial measurement unit with microelectromechanical system (MEMS) was designed in this study. Yaw angle estimation with magnetometers is disturbed by the perturbed magnetic field generated by coal rock structure and high-power equipment of shearer in automatic coal mining working face. Roll and pitch angles are estimated using the MEMS gyroscope and accelerometer, and the accuracy is not reliable with time. In order to eliminate the measurement error of the sensors and obtain the high-accuracy attitude estimation of the system, an unscented Kalman filter based on quaternion according to the characteristics of complementation of the magnetometer, accelerometer and gyroscope is applied to optimize the solution of sensor data. Then the gradient descent algorithm is used to optimize the key parameter of unscented Kalman filter, namely process noise covariance, to improve the accuracy of attitude calculation. Finally, an experiment and industrial application show that the average measurement error of yaw angle is less than 2° and that of pitch angle and roll angle is less than 1°, which proves the efficiency and feasibility of the proposed system and method. |
| first_indexed | 2024-03-10T16:06:19Z |
| format | Article |
| id | doaj.art-1027bcdc064f477b9913817e1c98a4bb |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T16:06:19Z |
| publishDate | 2020-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-1027bcdc064f477b9913817e1c98a4bb2023-11-20T14:48:20ZengMDPI AGSensors1424-82202020-09-012019545910.3390/s20195459A Portable Support Attitude Sensing System for Accurate Attitude Estimation of Hydraulic Support Based on Unscented Kalman FilterXuliang Lu0Zhongbin Wang1Chao Tan2Haifeng Yan3Lei Si4Dong Wei5School of Mechatronic Engineering, China University of Mining and Technology, Daxue Road, Xuzhou 221116, ChinaSchool of Mechatronic Engineering, China University of Mining and Technology, Daxue Road, Xuzhou 221116, ChinaSchool of Mechatronic Engineering, China University of Mining and Technology, Daxue Road, Xuzhou 221116, ChinaSchool of Mechatronic Engineering, China University of Mining and Technology, Daxue Road, Xuzhou 221116, ChinaSchool of Mechatronic Engineering, China University of Mining and Technology, Daxue Road, Xuzhou 221116, ChinaSchool of Mechatronic Engineering, China University of Mining and Technology, Daxue Road, Xuzhou 221116, ChinaTo measure the support attitude of hydraulic support, a support attitude sensing system composed of an inertial measurement unit with microelectromechanical system (MEMS) was designed in this study. Yaw angle estimation with magnetometers is disturbed by the perturbed magnetic field generated by coal rock structure and high-power equipment of shearer in automatic coal mining working face. Roll and pitch angles are estimated using the MEMS gyroscope and accelerometer, and the accuracy is not reliable with time. In order to eliminate the measurement error of the sensors and obtain the high-accuracy attitude estimation of the system, an unscented Kalman filter based on quaternion according to the characteristics of complementation of the magnetometer, accelerometer and gyroscope is applied to optimize the solution of sensor data. Then the gradient descent algorithm is used to optimize the key parameter of unscented Kalman filter, namely process noise covariance, to improve the accuracy of attitude calculation. Finally, an experiment and industrial application show that the average measurement error of yaw angle is less than 2° and that of pitch angle and roll angle is less than 1°, which proves the efficiency and feasibility of the proposed system and method.https://www.mdpi.com/1424-8220/20/19/5459support attitudeinertial measurement unitcoal miningunscented Kalman filterquaterniongradient descent |
| spellingShingle | Xuliang Lu Zhongbin Wang Chao Tan Haifeng Yan Lei Si Dong Wei A Portable Support Attitude Sensing System for Accurate Attitude Estimation of Hydraulic Support Based on Unscented Kalman Filter Sensors support attitude inertial measurement unit coal mining unscented Kalman filter quaternion gradient descent |
| title | A Portable Support Attitude Sensing System for Accurate Attitude Estimation of Hydraulic Support Based on Unscented Kalman Filter |
| title_full | A Portable Support Attitude Sensing System for Accurate Attitude Estimation of Hydraulic Support Based on Unscented Kalman Filter |
| title_fullStr | A Portable Support Attitude Sensing System for Accurate Attitude Estimation of Hydraulic Support Based on Unscented Kalman Filter |
| title_full_unstemmed | A Portable Support Attitude Sensing System for Accurate Attitude Estimation of Hydraulic Support Based on Unscented Kalman Filter |
| title_short | A Portable Support Attitude Sensing System for Accurate Attitude Estimation of Hydraulic Support Based on Unscented Kalman Filter |
| title_sort | portable support attitude sensing system for accurate attitude estimation of hydraulic support based on unscented kalman filter |
| topic | support attitude inertial measurement unit coal mining unscented Kalman filter quaternion gradient descent |
| url | https://www.mdpi.com/1424-8220/20/19/5459 |
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