Adaptive control of advance bracket support force in fully mechanized roadway based on neural network PID

Under excavation disturbance,high crustal stress in deep coal mining will lead to large-scale plastic failure and strong dynamic instability of deep surrounding rock,which lasts for a long time and seriously threatens the personal safety of coal miners at the excavation working face. In recent years,a lot of research have been carried on the variation law of deep surrounding rock pressure,surrounding rock control theory and advance support,but there are still many problems to be solved. In order to avoid the phenomenon of breakage and fragmentation of roadway surrounding rock, the support force of advance bracket should adapt to the change of surrounding rock pressure,so as to make full use of the self-supporting capacity of roadway surrounding rock. The structure of one kind self-forward advance bracket is in- troduced and its hydraulic control system is analyzed,and the mathematical model of the control system is established. The perfor-mance of the control system is studied with no control algorithm,traditional PID control algorithm and neu- ral network PID control algorithm. According to the geological conditions in Qishan mine,the mechanical coupling model of surrounding rock-advance bracket is established,and the curve of roadway surrounding rock pressure is ob- tained by FLAC3D simulation fitting. Taking the curve as the input,the self-adapting change rules of the advance brack- et support force to the roadway surrounding rock pressure are simulated when the traditional PID control algorithm and neural network PID control algorithm are used. The results show that compared with the traditional PID control algo- rithm,the settling time of support force control system with neural network PID control algorithm is about 2 s,which is shortened by 16 times,and the overshoot is about 6% ,and the dynamic performance is improved,and the surrounding rock pressure tracking error is only 0. 005 5,improved by 6. 8 times. The research results show that the advance brack- et control system based on the neural network PID control strategy can control the support force of the advance bracket adaptively with the pressure of the surrounding rock and its control effect is more advantageous than that with the tradi- tional PID control strategy.