Self-adaptive Adjustment System for Swing Speed of Boom-type Roadheader

As the main equipment of the coal mining industry, the boom-type roadheader's tunneling efficiency is mainly affected by the power of the cutting motor. In the traditional coal mining process, sudden changes in the hardness of the coal and rock will cause the power of the cutting motor to oscillate, thereby reducing the operating efficiency of the roadheader. In order to solve the problem of the power oscillation of the cutting motor when the coal hardness changes suddenly, a roadheader swing speed adjustment system is designed. The system mainly stabilizes the power of the cutting motor by adjusting the swing speed of the cantilever. The system first introduced the sliding window algorithm to solve the problem of BP neural network forgetting historical samples, and then used the BP neural network algorithm to optimize PID control, construct a BP-PID controller, and finally use the cutting current as the basis for sudden changes in coal hardness. The control performance of BP-PID control based on sliding window is simulated and analyzed. The results show that the BP neural network PID control system based on sliding window can quickly adjust the swing speed, stabilize the cutting motor power, reduce the oscillation amplitude, have better adaptability, and has more superior control performance than a single PID control system.