改进BP神经网络的综采设备协同控制方法  被引量:3

Collaborative Control Method of Fully Mechanized Mining Equipment Based on Improved BP Neural Network

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作  者:湛玉婕[1] ZHAN Yujie(College of Business Administration,Xuzhou College of Industrial Technology,Xuzhou 221000,China)

机构地区:[1]徐州工业职业技术学院工商管理学院,江苏徐州221000

出  处:《煤炭技术》2022年第10期207-209,共3页Coal Technology

基  金:江苏高校青蓝工程资助项目(苏教师[2019]3号);徐州工业职业技术学院科研项目(XGY2020EA03)。

摘  要:井下采煤作业环境复杂,刮板输送机负载经常突变,电机瞬间负荷忽增忽减,易造成电力资源浪费或者设备堵转和负重重启,还会对设备造成伤害,降低设备使用寿命。基于此,提出了一种基于BP神经网络的综采设备协同的控制方法,采用模糊控制与BP网络相结合,提高其学习能力,改善其收敛速度慢、易导致局部极值极小化等问题。建立了基于模糊BP神经网络的PID协同控制系统。实验表明:能够更好地解决采煤机与刮板输送机协同控制问题,节约能耗,避免了刮板输送机工作状态中负载量起伏变化,适应性强,提高了非正常工作状况下的快速恢复,实验结果较为理想。The underground coal mining operation environment is complex,the load of scraper conveyor often changes abruptly,the instantaneous load of the motor increases or decreases slightly,which may cause waste of power resources or the blocking and restarting of the equipment,and may also cause injury to the equipment and reduce the service life of the equipment.Based on this,a synergistic control method for fully-mechanized mining equipment based on BP network is presented.Fuzzy control is combined with BP network to improve its learning ability,slow convergence and easy to cause local minimum.A PID collaborative control system based on fuzzy neural network is established.Experiments show that the problem of coordinated control between shearer and scraper conveyor can be better solved,energy consumption can be saved,load fluctuation in the working state of scraper conveyor can be avoided,adaptability is strong,and fast recovery under abnormal working conditions can be improved.The experimental results are satisfactory.

关 键 词:协同控制 BP神经网络 模糊控制 

分 类 号:TP183[自动化与计算机技术—控制理论与控制工程]

 

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