[1]袁希文,冯江华,胡云卿,等.智轨电车自动循迹感知与控制系统[J].控制与信息技术,2020,(01):19.[doi:10.13889/j.issn.2096-5427.2020.01.003]
 YUAN Xiwen,FENG Jianghua,HU Yunqing,et al.Perception and Control Module of the Automatic Tracking System forAutonomous-rail Rapid Tram[J].High Power Converter Technology,2020,(01):19.[doi:10.13889/j.issn.2096-5427.2020.01.003]
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智轨电车自动循迹感知与控制系统()
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《控制与信息技术》[ISSN:2095-3631/CN:43-1486/U]

卷:
期数:
2020年01期
页码:
19
栏目:
“智能轨道快运系统”专刊
出版日期:
2020-02-05

文章信息/Info

Title:
Perception and Control Module of the Automatic Tracking System forAutonomous-rail Rapid Tram
文章编号:
2096-5427(2020)01-0019-08
作者:
袁希文冯江华胡云卿张新锐黄瑞鹏林 军袁 浩
(中车株洲电力机车研究所有限公司,湖南株洲 412001)
Author(s):
YUAN Xiwen FENG Jianghua HU Yunqing ZHANG Xinrui HUANG Ruipeng LIN Jun YUAN Hao
( CRRC Zhuzhou Institute Co.,Ltd., Zhuzhou, Hunan 412001,China )
关键词:
智轨电车图像识别深度学习自动循迹时间延迟模型预测控制
Keywords:
autonomous-rail rapid tram image recognition deep learning automatic tracking time delay model predictive control
分类号:
U461.6
DOI:
10.13889/j.issn.2096-5427.2020.01.003
文献标志码:
A
摘要:
自动循迹系统旨在实现智轨电车头轴自动跟随数字虚拟轨道行驶。其感知子系统解决了信息分离、标识、特征提取和连续数字轨道生成难题,在路面信息残缺、车辆随机振动条件下能够稳定形成数字轨道;其控制子系统在列车架构特殊、初始姿态随机情况下,实现了头轴可自主、精确地进入和跟踪数字轨道。文章首先介绍了自动循迹系统组成,提出一种基于深度学习的车道线识别与数字虚拟轨道生成算法;接着基于智轨电车非线性、大延时的特点,分别提出了考虑延时的PID控制法和MPC控制法。实车试验结果表明,采用所提出的方法,列车停站时头车与站台间隙保持在10 cm内,高速行驶时横向偏差控制在±15 cm内,达到了业内领先的自动循迹控制水平。
Abstract:
The automatic tracking system enables the transit front axle to automatically follow the digital virtual central line. Under the condition of incomplete road information and random vehicle vibration, its perception subsystem that builds a stable virtual track via image recognition solves the problem of information separation, identification, feature extraction and continuous digital virtual central line generation. Its control subsystem achieves automatic and accurate first axle entry and tracking of digital virtual center line, while normally the transit architecture is special and under random initial attitude. The principle and scheme of automatic tracking system were presented, and the digital virtual central line recognition algorithm based on deep learning was also introduced. Considering that autonomous-rail rapid transit is a complicated nonlinear with large time delay system, two automatic tracking control methods of PID and MPC considering time delay were derived in detail. Finally, the effectiveness of the algorithm was verified by both co-simulation and the real vehicle test. The results show that the precise docking clearance between the platform and the first carriage is kept at about 10 cm. When driving at high speed, the lateral deviation is controlled within ± 15 cm, achieving the industry leading level.

参考文献/References:

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备注/Memo

备注/Memo:
收稿日期:2019-09-30
作者简介:袁希文(1985—),男,博士,高级工程师,研究方向为车辆动力学控制、自动驾驶运动规划与控制。
基金项目:国家重点研发计划(2018YFB1201600)
更新日期/Last Update: 2020-03-26