Movable Surface Rotation Angle Measurement System Using IMU
In this paper, we describe a rotation angle measurement system (RAMS) based on an inertial measurement unit (IMU) developed to measure the rotation angle of a movable surface. The existing IMU-based attitude (tilt) sensor can only accurately measure the rotation angle when the rotation axis of the m...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-11-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/22/22/8996 |
| _version_ | 1797463867827159040 |
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| author | Changfa Wang Xiaowei Tu Qi Chen Qinghua Yang Tao Fang |
| author_facet | Changfa Wang Xiaowei Tu Qi Chen Qinghua Yang Tao Fang |
| author_sort | Changfa Wang |
| collection | DOAJ |
| description | In this paper, we describe a rotation angle measurement system (RAMS) based on an inertial measurement unit (IMU) developed to measure the rotation angle of a movable surface. The existing IMU-based attitude (tilt) sensor can only accurately measure the rotation angle when the rotation axis of the movable surface is perfectly aligned with the X axis or Y axis of the sensor, which is always not possible in real-world engineering. To overcome the difficulty of sensor installation and ensure measurement accuracy, first, we build a model to describe the relationship between the rotation axis and the IMU. Then, based on the built model, we propose a simple online method to estimate the direction of the rotation axis without using a complicated apparatus and a method to estimate the rotation angle using the known rotation axis based on the extended Kalman filter (EKF). Using the estimated rotation axis direction, we can effectively eliminate the influence of the mounting position on the measurement results. In addition, the zero-velocity detection (ZVD) technique is used to ensure the reliability of the rotation axis direction estimation and is used in combination with the EKF as the switching signal to adaptively adjust the noise covariance matrix. Finally, the experimental results show that the developed RAMS has a static measurement error of less than 0.05° and a dynamic measurement error of less than 1° in the range of ±180°. |
| first_indexed | 2024-03-09T17:59:45Z |
| format | Article |
| id | doaj.art-15d043412b8f4098a364dded6792d11d |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T17:59:45Z |
| publishDate | 2022-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-15d043412b8f4098a364dded6792d11d2023-11-24T09:59:10ZengMDPI AGSensors1424-82202022-11-012222899610.3390/s22228996Movable Surface Rotation Angle Measurement System Using IMUChangfa Wang0Xiaowei Tu1Qi Chen2Qinghua Yang3Tao Fang4School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200444, ChinaSchool of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200444, ChinaSchool of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200444, ChinaSchool of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200444, ChinaSchool of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200444, ChinaIn this paper, we describe a rotation angle measurement system (RAMS) based on an inertial measurement unit (IMU) developed to measure the rotation angle of a movable surface. The existing IMU-based attitude (tilt) sensor can only accurately measure the rotation angle when the rotation axis of the movable surface is perfectly aligned with the X axis or Y axis of the sensor, which is always not possible in real-world engineering. To overcome the difficulty of sensor installation and ensure measurement accuracy, first, we build a model to describe the relationship between the rotation axis and the IMU. Then, based on the built model, we propose a simple online method to estimate the direction of the rotation axis without using a complicated apparatus and a method to estimate the rotation angle using the known rotation axis based on the extended Kalman filter (EKF). Using the estimated rotation axis direction, we can effectively eliminate the influence of the mounting position on the measurement results. In addition, the zero-velocity detection (ZVD) technique is used to ensure the reliability of the rotation axis direction estimation and is used in combination with the EKF as the switching signal to adaptively adjust the noise covariance matrix. Finally, the experimental results show that the developed RAMS has a static measurement error of less than 0.05° and a dynamic measurement error of less than 1° in the range of ±180°.https://www.mdpi.com/1424-8220/22/22/8996angle measurementinertial measurement unitextended Kalman filterrotation axis direction estimation |
| spellingShingle | Changfa Wang Xiaowei Tu Qi Chen Qinghua Yang Tao Fang Movable Surface Rotation Angle Measurement System Using IMU Sensors angle measurement inertial measurement unit extended Kalman filter rotation axis direction estimation |
| title | Movable Surface Rotation Angle Measurement System Using IMU |
| title_full | Movable Surface Rotation Angle Measurement System Using IMU |
| title_fullStr | Movable Surface Rotation Angle Measurement System Using IMU |
| title_full_unstemmed | Movable Surface Rotation Angle Measurement System Using IMU |
| title_short | Movable Surface Rotation Angle Measurement System Using IMU |
| title_sort | movable surface rotation angle measurement system using imu |
| topic | angle measurement inertial measurement unit extended Kalman filter rotation axis direction estimation |
| url | https://www.mdpi.com/1424-8220/22/22/8996 |
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