基于支持向量机的智能ATO控车算法
赵磊1杜康2陈金叶2应承静2潘玉玲3
Intelligent Automatic Train Operation Vehicle Control Algorithm Based on Support Vector Machine
ZHAO LeiDU KangCHEN JinyeYING ChengjingPAN Yuling
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作者信息:1.苏州富欣智能交通控制有限公司，215163，苏州；
2.淮安市现代有轨电车经营有限公司，223001，淮安；
3.上海富欣智能交通控制有限公司，201203，上海
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Affiliation:Suzhou Fuxin Intelligent Transportation Solutions Co., Ltd., 215163, Suzhou, China
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关键词:
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Key words:
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DOI:10.16037/j.1007-869x.2020.10.045
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中图分类号/CLCN:U29-39
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栏目/Col:其他
摘要:
为解决ATO（列车自动运行）系统在实际线路实施过程中的适应能力差以及与车辆牵引制动性能耦合度过紧等问题，提出一种基于支持向量机(SVM)且具有训练模式和运营模式的智能ATO系统。该ATO系统直接采用车载设备性能参数、车辆性能参数、ATP（列车自动保护）/ATO曲线和线路参数等作为系统输入，通过基于SVM的机器学习算法计算ATO系统牵引和制动电压/电流输出参数。通过对实际线路ATO控车曲线数据的训练，调节SVM参数，得到最优智能ATO控车算法，且可利用该算法对实际线路环境下的控车命令进行预测。
Abstracts:
In order to solve the problems like poor adaptability in the actual implementation of automatic train operation (ATO) system, and the tight coupling of vehicle traction and braking performance, an intelligent ATO system based on support vector machine (SVM) with training mode and operation mode is proposed. The ATO system directly takes on-board equipment performance parameters, vehicle performance parameters, ATP (automatic train protection)/ATO curve and line parameters as the system input, and uses the machine learning algorithm based on SVM to calculate ATO system traction and braking voltage/current output parameters. The optimal intelligent ATO control algorithm is obtained by training with the actual ATO control curve data and adjusting the SVM parameters. The algorithm can also be used to predict the vehicle control commands under the actual line environment.
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