Research On Adaptive Cutting Planning And Control Technology Of Shearer

Shearer is the key equipment of intelligent fully mechanized mining face.At present,the automatic cutting control of shearer is mainly realized through "teaching memory cutting".There are some problems,such as too long teaching time,cumbersome operation process and not adapted to the fluctuation and change of coal seam,so it is urgent to solve the problems of shearer automatic cutting trajectory planning and adaptive control.Aiming at the MG1100/3030-GWD shearer with the maximum mining height of 8.8 m,this thesis designs a cutting trajectory planning method for the top and floor shearer,and proposes a trajectory tracking control strategy based on linear quadratic regulator-Error Band Control(LQR-EBC),and carries out testing and verification.The main research content and achievements of this thesis are as follows:(1)Analyzed the basic structure and working principle of the shearer,established a pose measurement model for the shearer,and designed the overall framework of the adaptive cutting system for the shearer from software,communication,and functions.(2)Based on geological exploration data,a three-dimensional static model of coal seam was established by Kriging interpolation algorithm.Combined with the cutting track of top and floor planned by the static model of coal seam and the historical cutting track information of top and floor,a dynamic trajectory correction algorithm of long short-term memory network(LSTM)based on trajectory extraction is designed.The corrected roof planning cutting roof and floor trajectory error was mainly distributed between 0 and 0.1m,meeting the requirements of adaptive cutting planning.(3)The fusion positioning model of shearer based on Kalman filter is established.Based on the kinematics constraints of shearer,a hybrid control strategy of trajectory tracking based on LQR-EBC is proposed.The improved marine predator algorithm is used to optimize it.The trajectory tracking control strategy is simulated and verified.The trajectory tracking control effect is evaluated through the gray correlation evaluation model.The results show that the average tracking errors of the top and bottom plate cutting trajectory based on LQR-EBC hybrid control are 0.021 and 0.020 meters,meeting the requirements of the cutting drum height adjustment control.(4)I have built an adaptive cutting experimental platform for coal mining machines,developed an intelligent monitoring system software for coal mining machines,and conducted remote monitoring function testing,operation panel control testing,and adaptive cutting testing.The test results show that the remote monitoring system of the coal mining machine meets the design requirements,and the remote control function of the operation panel is normal.The average error of the top cutting trajectory in the adaptive cutting mode is 0.024 m,and the average error of the bottom cutting trajectory is 0.021 m.This verifies the feasibility of the adaptive cutting system of the coal mining machine proposed in this thesis.There are 91 figures,31 tables and 94 references in this thesis.