Adaptive Cutting Control for Roadheaders Based on Performance Optimization

Abstract

Aiming at addressing the problems of high specific energy consumption for cutting and slow response to the change of hardness in the control of existing mining roadheaders, an adaptive variable speed cutting control method based on cutting performance optimization is proposed by analyzing the working principle of roadheaders. Firstly, cylinder pressure and motor current are invoked as the criteria to judge load changes. Particle swarm optimization is utilized to optimize the cutting parameters under different impedance. Then, the relation between cutting speed, motor current and cylinder pressure is established by using fuzzy neural network to train cutting parameters and identification parameters under different conditions. Finally, the vector control of motor and electro-hydraulic servo valve is used to control the cutting speed. The results show that the cutting unit can adapt to different load signals and always keep the roadheader in the optimal working state. The rotation speed regulation of the cutting head reaches the stable state after 0.05 s, with the overshoot of 1.42%. The swing speed regulation of the cutting head reaches the stable state after 1 s, with the overshoot of 5.3%. Conclusions provide a basis for improving the cutting efficiency and prolonging the working life of the roadheader.

Keywords

• performance optimization
• adaptive
• fuzzy neural networks
• SVPWM
• servo valve