矿用液压支架自动化控制的优化分析
Optimization of Automation Control for Mining Hydraulic Supports
李博1
作者信息
- 1. 晋能控股装备制造集团大同机电装备有限公司中央机厂,山西 大同 037000
- 折叠
摘要
在煤矿井下开采中,液压支架作为综采工作面重要的支护设备,其支护效果直接影响到矿井开采的安全性.在煤矿开采智能化、自动化水平不断提高的情形下,需要做好液压支架的自动化控制工作,降低人工移架的强度,提高巷道支护的安全性.为此,从提高支架自动化控制精度出发,基于支架自动化控制的概述,应用BP(Back Propagation,逆向传播)神经网络算法构建支架姿态自动化预测控制系统,对支架的姿态进行准确的预测,进而提高液压支架的自动化控制精度.最后,经过技术试用,证实姿态自动化控制系统能提高支架的自动化控制水平.
Abstract
In the underground mining of coal mines,hydraulic supports are important support equipment for fully mechanized mining faces,and their support effect directly affects the safety of mining.In the context of the continuous improvement of intelligence and automation level in coal mining,it is necessary to do a good job in the automation control of hydraulic supports,reduce the intensity of manual support,and improve the safety of tunnel support.Therefore,starting from improving the accuracy of support automation control,based on the overview of support automation control,the BP(Back Propagation)neural network algorithm was applied to construct an automatic prediction control system for support posture,accurately predicting the posture of the support,and thereby improving the automation control accuracy of hydraulic supports.Finally,through technical trials,it is confirmed that the attitude automation control system can improve the automation control level of the support.
关键词
液压支架/自动化控制/优化方案/姿态解算Key words
hydraulic support/automation control/optimization scheme/attitude algorithm引用本文复制引用
出版年
2024
NETL
NSTL
万方数据