Abstract
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.
Highlights
Hydraulic support is an important safety support equipment in the automatic coal mining working face, which is of great significance to the safety of coal miners and the normal operation of coal mining equipment
This paper proposes a new attitude estimation method for measuring the pitch angle, yaw angle and roll angle of hydraulic support based on the characteristics of complementation of accelerometers, gyroscopes and magnetometers, which eliminates the errors derived from the microelectromechanical system (MEMS) sensors by the quaternion-based unscented Kalman filter (UKF)
In order to improve the performance of the support attitude estimation algorithm, the gradient descent algorithm is used for tuning the key parameter of the unscented Kalman filter, i.e., process noise covariance Q
Summary
Hydraulic support is an important safety support equipment in the automatic coal mining working face, which is of great significance to the safety of coal miners and the normal operation of coal mining equipment. We try to explore a new approach for estimating directly the support attitude of hydraulic support with comprehensive consideration of estimation accuracy, measurement method, universality and sensor size. Through the above research on attitude estimation of nonlinear systems, and considering the estimation accuracy, calculation speed and real-time performance of algorithm, UKF is proved to be a powerful technique for estimating attitude angle and a superior alternative to the EKF and PF in various nonlinear system filtering [37,38]. A support attitude sensing system with a character of intrinsic safety is designed to measure the support attitude of hydraulic support in the special application scenarios with a large demand for the number of sensors and complex environment.
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