The coupling relationship analysis and control of the roadheader’s cutting and supporting structures

To solve the problem that the roadway excavation trajectory deviates due to the poor posture control accuracy of the roadheader, which affects the safety and efficiency of coal mine production. A fuzzy neural network PID posture control method for the posture adjustment of the roadheader is proposed. Firstly, the influence and compensation of cutting and support on the roadway quality are analyzed, and the relationship between the posture angle error of the roadheader and the roadway section is obtained. Based on the kinematics posture and hydraulic model, the transfer function structure of the system is determined, and the fuzzy neural control method to control the system of roadheader is proposed. A PID control system for posture adjustment is designed using the strong nonlinear adaptability of fuzzy control and the self-learning ability of neural network. The pitch angle control test platform of EBZ-55 roadheader was built, and several sets of pitch angle control tests were carried out under different test conditions. Results show that the fuzzy neural network PID control algorithm has a maximum steady error of 0.804 mm, the longest response time for the system to reach a steady state is 1.113 s, and the maximum standard deviation is 0.159 in various simulation conditions. It can meet the precision requirements of the system under various simulation conditions and has obvious advantages in performance compared with the fuzzy PID control method. The method can provide effective technical support and reference for posture control of roadheader and large hydraulic equipment.