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

Abstract

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.

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