Optimization of Automation Control for Mining Hydraulic Supports
In the underground mining of coal mines,hydraulic supports are important support equipment for fully mechanized mining faces,and their support effect directly affects the safety of mining.In the context of the continuous improvement of intelligence and automation level in coal mining,it is necessary to do a good job in the automation control of hydraulic supports,reduce the intensity of manual support,and improve the safety of tunnel support.Therefore,starting from improving the accuracy of support automation control,based on the overview of support automation control,the BP(Back Propagation)neural network algorithm was applied to construct an automatic prediction control system for support posture,accurately predicting the posture of the support,and thereby improving the automation control accuracy of hydraulic supports.Finally,through technical trials,it is confirmed that the attitude automation control system can improve the automation control level of the support.
NETL
NSTL
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