Research On Collaborative Control Technology Of Shearer And Support In Intelligent Fully Mechanized Face

Coal is the most important fossil energy source in my country,and the realization of the automation of fully mechanized mining face is a necessary prerequisite for ensuring the efficient production and safe operation of coal mines.At present,the automatic control system of fully mechanized mining face has initially completed the autonomous control of shearers and hydraulic supports,and has realized remotely intervention in the geological environment of different coal seams.However,it is difficult to solve all the problems in the field only for the control of a single type of equipment.There are still shortcomings in the coordination,including cooperation of multiple equipment,intelligent control of fully mechanized mining equipment,and completion of different mining processes.Therefore,it has great significance to study the shearer-support cooperative control technology to realize the shearer self-adaptive traction speed regulation and hydraulic support following automation under different coal seam conditions.The main contents of this paper are as follows:1.According to the basic structure and theoretical technology of the current drum shearer and support,a mathematical model for the coordinated control of the shearer and the support is established,obtain the shearer cutting traction model and the shearer-support cooperative control model.Use a variety of data mining algorithms to optimize the comparison,and compare the various data of the shearer.The parameters are input variables,and the traction speed is the optimized output variable,so as to achieve the purpose of cutting-traction speed control of the shearer with the best overall performance of coal mining under different steady-state cutting conditions.Set coal mining productivity and cutting specific energy consumption as the model sub-objectives,achieving the goal of high efficiency and low consumption.2.Use the output speed and environmental parameters of the shearer as the input part of the frame coordination system,the distance of the support frame and the extension speed as the output part of the system.The BP neural network controller is used to calculate the error between the actual output and the ideal output and perform feedback adjustment.The genetic algorithm is used to update the thresholds and weights of each layer of the iterative model,and finally the stent controller is used to issue the action command.Through the establishment of GA-BP combined model,the effect of fitting nonlinearity under dynamic environment is studied,including system error analysis,optimal selection of model parameters,and fitness curve analysis.3.When the shearer is cutting,which the speed is too fast,it causes the support guard plate to extend and retract in time and cause damage.Therefore,it is necessary to track and detect the shearer equipment under the coordinated control of the fully mechanized mining face.First,preprocess the images collected by the camera,including denoising technology,image grayscale,image binary,etc.Then,the method of template matching is used to track and detect the shearer,which provides a technical basis for realizing remote intervention under the coordinated control state of the shearer-support4.The experimental platform of the stent controller is built,and the remote operation commands to the stent controller are realized.The research on the dynamic characteristics of the frame moving motion and the analysis and comparison of the error of the frame moving distance have been carried out,which verifies that the GA-BP combined model has good accuracy.