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.
Highlights
As an essential equipment in a fully-mechanized coal mining working face [1], a hydraulic powered roof support connects with the scraper chain conveyor through the pushing link
The pushing distance is measured by a displacement sensor which is embedded in the hydraulic pushing cylinder to avoid being exposed in extreme conditions
The processing noise covariance and observation noise covariance are set through experience, which sometimes leads to large estimation error
Summary
As an essential equipment in a fully-mechanized coal mining working face [1], a hydraulic powered roof support connects with the scraper chain conveyor through the pushing link. Is proposed to process the inertial information To make it easy for jumping out of the local extreme, the basic FOA is modified by adding a perturbation intensity through a predict-update mechanism when falling into a local extreme. Bearing the above observations in mind, an inertial sensor is utilized to measure the inertial information of the pushing link, and an improved fruit fly algorithm optimized Kalman filter (FOA-KF). To make it easier for jumping out of the local extreme, the basic FOA is modified by adding a perturbation intensity through a predict-update mechanism when falling into a local extreme. A simulation and application for the pushing distance estimation proves the effectiveness and superiority of the proposed method
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