Swing Speed Control Strategy of Fuzzy PID Roadheader Based on PSO-BP Algorithm

Aiming at the problem that the boom-type roadheader cannot quickly adjust the cutting swing speed to adapt to the hardness of the coal and rock when the coal hardness changes in coal mines, a control strategy for the driving swing speed is proposed. In this strategy, firstly, the PSO-BP neural network is used to construct a cutting load recognizer to provide a basis for adjusting the cutting swing speed of the roadheader; secondly, the PID control is optimized based on the fuzzy algorithm, and the fuzzy PID controller is established to improve the regulation of the cutting The efficiency of the swing speed; Finally, the roadheader swing speed simulation control system model is built in Matlab/Simulink, and the proposed roadheader cutting swing speed control strategy is simulated. The simulation experiment results show that the roadheader swing speed adjustment system using PSO neural network algorithm combined with fuzzy PID control has significantly improved response speed and control accuracy, and has good superiority and stability. The strategy based on particle swarm BP neural network algorithm combined with fuzzy PID control can provide certain theoretical guidance for stabilizing the cutting motor power of the roadheader and improving the efficiency of roadway work.