A Fruit Fly-Optimized Kalman Filter Algorithm for Pushing Distance Estimation of a Hydraulic Powered Roof Support through Tuning CovarianceReportar como inadecuado


A Fruit Fly-Optimized Kalman Filter Algorithm for Pushing Distance Estimation of a Hydraulic Powered Roof Support through Tuning Covariance


A Fruit Fly-Optimized Kalman Filter Algorithm for Pushing Distance Estimation of a Hydraulic Powered Roof Support through Tuning Covariance - Descarga este documento en PDF. Documentación en PDF para descargar gratis. Disponible también para leer online.

1

School of Mechatronic Engineering, China University of Mining and Technology, Xuzhou 221116, China

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School of Information and Electrical Engineering, China University of Mining and Technology, Xuzhou 221116, China

3

School of Computer Sciecnce and Technology, Harbin Institute of Technology, No. 92 West Dazhi Street, Harbin 150001, China





*

Author to whom correspondence should be addressed.



Academic Editor: César M. A. Vasques

Abstract To measure the pushing distance of a hydraulic-powered roof support, and reduce the cost from a non-reusable displacement sensor embedded in pushing a hydraulic cylinder, an inertial sensor is used to measure the pushing distance, and a Kalman filter is applied to process the inertial data. To obtain better estimation performance, an improved fruit fly optimization algorithm IFOA is proposed to tune the parameters of the Kalman filter, processing noise covariance Q and observation noise covariance R. The key procedures of the proposed method, including state-space model, fitness function, and Kalman filter implementation, are presented. Finally, an artificial signal is utilized to verify the feasibility of the proposed method, and the tuning results of other algorithms, particle swarm optimization PSO, genetic algorithm GA, basic FOA, and 3D-FOA are compared. The proposed method is also applied in the pushing distance estimation scenario. The simulation and application results prove the effectiveness and superiority of the proposed method. View Full-Text

Keywords: Kalman filter; fruit fly optimization algorithm; hydraulic powered roof support; state-space model Kalman filter; fruit fly optimization algorithm; hydraulic powered roof support; state-space model





Autor: Lin Zhang 1, Zhongbin Wang 1,* , Chao Tan 1, Lei Si 1,2, Xinhua Liu 1 and Shang Feng 3

Fuente: http://mdpi.com/



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