基于群体智能算法的巷道快速掘进自动化控制研究

Research on Automatic Control of Rapid Tunnel Excavation Based on Group Intelligence Algorithm

以提高掘进效率、优化矿山资源的开采过程为目的,该文提出基于群体智能算法的巷道快速掘进自动化控制方法。采集掘进机截割电机的负载油缸压力、截割臂振动加速度和截割电机电流数据,将其输入到Q-RBF神经网络模型内,通过该模型迭代输出掘进机截割臂载荷信号,对该载荷信号转换成摆速后得到掘进机截割臂期望摆速,将期望摆速与实时摆速同时输入到模糊PID控制器内,并对模糊PID控制器参数进行整定处理,输出掘进机截割臂摆速控制参数,实现巷道快速掘进自动化控制。实验表明,该方法可实现掘进机截割臂摆速控制,超调数值较小,应用效果较为显著。

In order to improve the excavation efficiency and optimize the mining process of mine resources,an automatic control method of roadway rapid excavation based on swarm intelligence algorithm is proposed.The data of load cylinder pressure,vibration acceleration of cutting arm and current of cutting motor of roadheader are collected and input into Q-RBF neural network model.The load signal of cutting arm of roadheader is iteratively output through this model,and the expected swing speed of cutting arm of roadheader is obtained after the load signal is converted into swing speed.The expected swing speed and real-time swing speed are input into fuzzy PID controller at the same time,and the parameters of fuzzy PID controller are adjusted to output swing speed control parameters of cutting arm of roadheader,thus realizing automatic control of rapid roadway excavation.The experiment shows that this method can realize the swing speed control of the cutting arm of the roadheader,and the overshoot value is small,and the application effect is remarkable.