Optimization design of pushing distance estimation of hydraulic support using the Kalman filter with the covariance improvement. (May 2019)

• Record Type: Journal Article
• Authors: Lu, Xuliang; Wang, Zhongbin; Yan, Haifeng; Si, Lei; Tan, Chao
• Is Part Of: Advances in mechanical engineering. Volume 11:Number 5(2019)
• Publication Date: 2019-05
• Subjects: Pushing displacement -- hydraulic support -- pushing straightness -- fly optimization algorithm -- Kalman filter -- automation level; Mechanical engineering -- Periodicals; 621.05
• DOI: 10.1177/1687814019850720
• Languages: English
• ISSNs: 1687-8132

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.