Research on pitch control of coal mine roadheader based on fuzzy neural network PID

Abstract

Currently, PID control method is mainly used for the pitch control of coal mine roadheader, and the control precision is not high in the case of time-varying and nonlinear hydraulic system during the pitch control of roadheader. The pitch control of roadbeader is realized by controlling the stroke of the hydraulic cylinder. Combining the traditional PID algorithm with fuzzy control and neural network, the accuracy of the stroke control of the hydraulic cylinder can be effectively improved. In order to solve the above problems, a pitch control method for coal mine roadheader based on fuzzy neural network PID is proposed. By analyzing the kinematic relationship of the support part of the roadheader, the mathematical relationship between the pitch angle and the hydraulic cylinder of the support part is obtained. The working principle of the pitch control hydraulic system of the roadheader is introduced, and the hydraulic system and its transfer function model are established. The method combines fuzzy control with neural networks to form a fuzzy neural network. The method optimizes PID control parameters by using the fuzzy neural network. The method combines the mathematical model of the support mechanism and the transfer function model of the hydraulic system to establish a fuzzy neural network PID control model for the pitch angle of the roadheader. It achieves automatic and precise control of the pitch mechanism of the coal mine roadheader. This method can make the pitch mechanism of the roadheader reach the preset position more quickly and accurately, solving the time-varying and nonlinear problems in the pitch control of roadheader. The simulation results show that the fuzzy neural network PID control algorithm reduces tracking errors by 69.34% and 74.49% respectively compared to fuzzy PID and PID control algorithms. The method simulates the pitch control of coal mine roadheaders under sudden and following working conditions through hydraulic cylinder displacement control. The results show that compared with fuzzy PID and PID control algorithms, the fuzzy neural network PID control algorithm has the smallest pitch control tracking error, shortens the average response time to position signals by 27.22% and 50.33% respectively, and has better dynamic control performance.

Keywords

• roadheader pitch control
• pitch angle
• fuzzy neural network pid
• hydraulic system
• hydraulic cylinder displacement control
• support mechanism