With the continuous improvement of the mechanization level of coal mining,it is necessary to control the speed regulation of shearers according to the actual working conditions to achieve energy-saving upgrading.Therefore,based on the load prediction of scraper conveyors,the shearer speed regulation control system was designed,and then the load prediction model of scrapers was built with reference to the application of wavelet neural network,and data support was provided for the shearer speed regulation model with the collected current data.Secondly,after determining the speed regulation range of shearer under different loads,the Elman neural network was used rationally to complete the speed regulation strategy research of shearer.Finally,the corresponding industrial test was carried out,and the test data showed that the speed control of the shearer had higher accuracy and faster response speed,which can meet the needs of mining.
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