Adaptive control of setting load of hydraulic support based on BP neural network PID

The setting load of hydraulic support plays an important role in the roofcontrolThere are two methods to control the setting load of hydraulic support，one is openloop control by manual control valve of three position four port，the other is pilot control by solenoid directional control valve of two position three portHowever，these two methods can hardly make the setting load reach the expected value and remain stableEven when the expected value is reached，pressure drop and fluctuation generally existBased on this，a mathematical model of electrohydraulic force control system is established，then the stability of the system is analyzed by using MATLABIt is obtained that there are no openloop zeros and poles in the right half S plane of the PoleZero diagram of the system，so the system is a minimum phase system；the number of cycles of counterclockwise winding（-1，j0） from the Nyquist diagram is 0，and the system phase margin is 941° and the amplitude margin is 107 dB，so the system is stable；the step response is stable for 115 s，the impulse response is stable for 90 sAn adaptive PID control method based on BP Neural Network is proposed，and a threelayer neural network control model is establishedQuadratic performance index is used to control errorThe weight coefficients of the output and hidden layers are updated by using supervised Hebb learning rules and gradient descent algorithmThen three control parameters of the PID controller are obtained by trainingThe simulation results show that：it takes about 885 s for the setting load to reach the expected value and maintain stability when the expected input is step signal，and 91 s when the expected input is the square wave signalCompared with no BP neural network PID control，the response time is increased by about 13 times.