Optimization design of pushing distance estimation of hydraulic support using the Kalman filter with the covariance improvement

Abstract

The accuracy of pushing displacement of hydraulic support determines the pushing ability, which is the key factor that affects the pushing straightness of the hydraulic support. In order to further improve the accuracy of pushing displacement, considering the inertial sensor as the tools for measuring the pushing distance, an improved filtering method is proposed based on the improved fruit fly optimization algorithm and Kalman filter. The parameters of the Kalman filter, processing noise covariance Q and observation noise covariance R, are optimally designed by the improved fruit fly optimization algorithm to accurately perceive the inertial data. Finally, the feasibility of the proposed method with tuning the Q and R has been verified by comparing with the tuning results of other algorithms. An integrated filtering design method of pushing distance and key parameter analysis of algorithm in the pushing distance perception of hydraulic support are formed, which have theoretical and practical values, and improve the automation level of hydraulic support and the straightness of the fully mechanized coal mining working face.

Pushing Pose Sensing of Underground Mobile Supporting Robot

“…At present, the sensing approach for pose and straightness of hydraulic support mainly focus on simple on-site pull wire and infrared beam measurement. With the deep integration of intelligent detection technology into coal mine production, the inertial navigation technology [6,7], Unity3D technology [8][9][10][11], and visual technology [12,13] have been used gradually. Zhang et al and Chen et al [3,14] proposed a pose monitoring method of hydraulic support based on multisensor fusion technology.…”

Optimization of Process Control Parameters for Fully Mechanized Mining Face Based on ANN and GA

“…Due to the late start of our country's research on the automation and intelligence of fully mechanized mining faces. At present, the research is mainly about the optimization of the performance of a single device or the optimization of the reasonable cooperation between two devices at the equipment level, such as the cutting mode recognition of the shearer [2], study on the hydraulic support displacement accuracy [3], coordinated control research on driving the sprocket of the scraper conveyor under heavy load [4], and the research on the speed coordinated control of coal shearer and scraper conveyor [5]. In theory, improving the performance of a single device can play a role in improving the production capacity of the mining face.…”