摘要
针对铁路散装物料装车过程存在的装载不均匀和装载精准度低等问题,设计了基于时间卷积网络(TCN)的散装物料精准装车系统,运用多智能体协同控制思想和分层并发架构,采用静态轨道衡修正模型和基于TCN的车辆速度预测模型计算目标速度,结合基于径向基(RBF)神经网络的控制策略,完成装车全过程的车辆参数精确检测和运动速度精准控制。现场应用表明:该系统显著提高了装车精度,装载误差低于0.2%,大幅缩短了装车所需的时间,实现了无偏载精准装车。
Aiming at the problems of uneven loading and low accuracy of loading in the loading process of bulk materials in railway, a precise loading system of bulk materials based on temporal convolutional network(TCN) was designed. With multi-agent collaborative control thought and hierarchical concurrent architecture, using static rail weighbridge correction model and vehicle speed prediction model based on TCN to compute target speed, combined with the control strategy based on radial basis function(RBF) neural network, completed the whole process of loading parameters accurate detection and vehicle speed precision control. The field application shows that the loading precision and the loading error are significantly improved, and the loading error is less than 0.2%, and the time required for loading is greatly shortened, realizing the precise loading without bias.
作者
李敬兆
叶桐舟
欧阳其春
王翼宁
陆正兴
Li Jingzhao;Ye Tongzhou;Ouyang Qichun;Wang Yining;Lu Zhengxing(Anhui University of Science and Technology,Huainan 232001,China;Huaibei Mining Co.,Ltd.,Huaibei 235000,China;Jiangsu Liyuan Automation Engineering Co.,Ltd.,Jingjiang 214500,China)
出处
《煤矿机械》
2021年第3期63-65,共3页
Coal Mine Machinery
基金
国家自然科学基金项目(51874010)
北京理工大学高精尖机器人开放性研究项目(2018IRS16)
物联网关键技术研究创新团队(201950ZX003)。
