Steering angle adaptive estimation system based on GNSS and MEMS gyro

• Published in: Computers and electronics in agriculture 2018-10, Vol.153, p.196-201
• Main Authors: Miao, Cunxiao, Chu, Huanxin, Cao, Juanjuan, Sun, Zhihui, Yi, Ranran
• Format: Article
• Language: English
• Subjects: Adaptive filters; Adaptive Kalman filter; Adaptive systems; Antennas; Contact angle; Double GNSS antennas; Errors; Global navigation satellite system; Gyroscopes; Hall effect; Installations; Kalman filters; Lever-arm; Matrix; MEMS gyroscope; Microelectromechanical systems; Navigation satellites; Sensors; Steering; Steering angle
• ISSN: 0168-1699; 1872-7107
• DOI: 10.1016/j.compag.2018.08.019

•A steering angle measurement system based on the double GNSS and two MEMS gyroscopes is proposed.•The system uses the error equation of steering angle and the vehicle kinematics model to build an adaptive Kalman filter.•The lever-arm compensation is proposed to correct the speed error when calculate the measurement of Kalman filter. In automatic driving system research of precision agriculture, the wheel steering angle was usually measured by the non-contact absolute angle sensor such as Hall-effect sensor and angle encoder sensor. The problem of those angle sensors was the complex mechanical structure which result in the difficulty of installation and maintenance. However, the gyroscope had the advantages of simple installation and long working life. But the gyroscope bias causes the error accumulates over time in automatic driving system. To solve this problem, a wheel steering angle measurement system based on double Global Navigation Satellite System (GNSS) antennas and two Micro-Electro-Mechanical System (MEMS) gyroscopes was proposed. The double GNSS antennas system provided the speed, attitude angle, latitude and longitude of the vehicle. In addition, one MEMS gyroscope was mounted on the wheel and measured the steering rate and another MEMS gyroscope was mounted on the vehicle to measure the heading angular rate. Meanwhile, an adaptive Kalman filter using the above data was designed to correct the errors of integration of the gyroscope data. The adaptive filter could adjust the noise matrix according to the changes of system noise caused by road conditions and different tools. At the same time, the lever-arm compensation algorithm was used to solve the speed error caused by lever-arm. Experiment was carried out to verify the effectiveness of the system. The average error of the straight line experiment was 0.06 and the error variance was 0.215 and the curve line experiment’s mean error was 0.746 and the error variance was 0.908. The result of the experiments showed that this system could meet the request of automatic driving system.