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

Abstract

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.

Keywords

• hydraulic support
• leg
• setting load
• neural network
• self-adaption