| The auto-steering technology of the Shearer rocker is the key to improve the degree ofautomation of mechanized mining face. The objective of Shearer’s drum control is to makethe Shearer drum can well adapted to seam change, and then accurately track the targettrajectory curve. Meanwhile it has a great significance to increase productivity, improveequipment reliability, extend the longevity of the machine and protect the safety of coalproduction.In this paper, on the basis of in-depth study about the auto-steering technology of theShearer rocker, analysis of the working state of Shearer in the actual working conditions, andthen establishes the coordinate of mining working-face system. The Elman neural networkwas established and trained by using the parameters of Shearer frist cutting. In the process ofShearer automatic adjustment-height, the cutting parameters are input to the trained networkstructure, the network output control the Shearer’s drum for lifting. In the automatic adjustingprocess to add artificial auxiliary adjusting function, and can switching between the automaticmode and manual mode. In the process of automatic adjusting height has realised artificialauxiliary. The artificial intervention data was processed using the RBF neural networkalgorithm. The Shearer cutting attitude was predicted along the advancing direction of coalface by using Elman neural network. The VB6.0as the controller, Matlab as the server, tocomplete the design of the Shearer automatic adjusting control. Finally, process simulationabout the Shearer memory cutting, which included the frist cutting of Shearer, the treatmentof frist cutting data, automatic adjusting process, manual intervention in the process ofautomatic, manual data processing, prediction of shearer’s attitude, and so on. The results areproved the availability of the control method of Shearer’s drum lifting based on Elman neuralnetwork, artificial intervention and shearer attitude prediction. |
PDF Full Text Request