Nonlinear compensation-based self-positioning method for rail transit train

HAN Zhixing

doi:10.13890/j.issn.1000-128X.2023.02.015

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Nonlinear compensation-based self-positioning method for rail transit train

Electric Traction and Control | 更新时间：2024-08-02

- Nonlinear compensation-based self-positioning method for rail transit train
- Electric Drive for Locomotives Issue 2, Pages: 131-135(2023)
- 作者机构：
  
  山西职业技术学院电气自动化工程系，山西太原　　030006
- 作者简介：
- 基金信息：
- DOI：10.13890/j.issn.1000-128X.2023.02.015
  CLC： U2;U228.1
- Published：10 March 2023，
  
  Received：30 May 2021，
  
  Revised：16 January 2023，
- 稿件说明：
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韩芝星. 基于非线性补偿的轨道交通列车自定位方法研究[J]. 机车电传动, 2023(2): 131-135.

HAN Zhixing. Nonlinear compensation-based self-positioning method for rail transit train[J]. Electric Drive for Locomotives,2023(2): 131-135.
韩芝星. 基于非线性补偿的轨道交通列车自定位方法研究[J]. 机车电传动, 2023(2): 131-135. DOI： 10.13890/j.issn.1000-128X.2023.02.015.

HAN Zhixing. Nonlinear compensation-based self-positioning method for rail transit train[J]. Electric Drive for Locomotives,2023(2): 131-135. DOI： 10.13890/j.issn.1000-128X.2023.02.015.

摘要

列车精准定位对列车运营安全有着十分重要的意义。在某些国外地铁项目中，受限于技术体系的成熟度和设计成本，常规的GPS定位和CBTC定位技术无法满足项目对列车定位的需要，文章为此提出一种基于非线性补偿的轨道交通列车自定位方法。该方法能够在不增加新设备（如GPS、CBTC等）的情况下，仅依赖于车载既有设备实现列车位置的精确定位。所提出的方法系统组成简单、成本低、精度高、可靠性高（不受环境影响），具备推广意义。具体思路为：首先利用神经网络建立列车瞬时速度的补偿模型，通过补偿模型提升列车瞬时速度的精度；然后根据补偿后的列车实时运行速度与列车运行时间作积分累积得到列车实时的运行距离，再根据列车的固定运营线路图，计算得到列车距下一站的距离，实现定位。试验证明，补偿后的定位精度小于0.2 m，较补偿前提高了3~4倍。

Abstract

Accurate positioning of trains is of great importance to the safety of train operation. In some foreign subway projects

limited by the maturity of technical system and control of design cost

conventional GPS and CBTC positioning technologies cannot meet the demand of train positioning in the projects. Therefore

a nonlinear compensation based self-positioning method for rail transit trains is proposed. This method can achieve precise positioning of trains solely relying on existing onboard equipment without adding new equipment such as GPS

CBTC

etc. The proposed method features simple composition

low cost

high accuracy

and high reliability (free from environmental affection)

which is worth further promotion. Firstly

the specific process used neural networks to establish a compensation model for the instantaneous speed of trains and improve the accuracy of train instantaneous speed through the compensation model

and then the real-time running distance of the train was obtained by integrating the compensated real-time speed and operating time of the train. Afterwards

the distance of the train to the next station was calculated based on the fixed train operation diagram

thus to realize positioning. The experimental results show that after compensation the positioning accuracy is less than 0.2 m

upgraded by 3-4 times of the accuracy before compensation.

关键词

轨道交通列车定位神经网络非线性补偿

Keywords

rail transittrain positioningneural networknonlinear compensation

references

张珊, 李辉, 唐海周. “当前” 模型自适应卡尔曼滤波在有轨电车定位中的应用[J]. 机车电传动, 2020(2): 129-133.

ZHANG Shan, LI Hui, TANG Haizhou. The application of self-adaption Kalman filtering algorithm based on the current statistic model in the tram’s GPS positioning[J]. Electric Drive for Locomotives, 2020(2): 129-133.

江帆, 王雨, 李鹏, 等. 一种基于GPS速度的机车自动轮径校正方法[J]. 机车电传动, 2019(2): 144-147.

JIANG Fan, WANG Yu, LI Peng, et al. A method of locomotive automatic wheel diameter correction based on GPS speed[J]. Electric Drive for Locomotives, 2019(2): 144-147.

王强. LKJ-15型列车运行监控系统辅助测速定位技术[J]. 机车电传动, 2019(1): 126-128.

WANG Qiang. Application of auxiliary speed measurement and positioning technology for LKJ-15 train operation monitoring system[J]. Electric Drive for Locomotives, 2019(1): 126-128.

张俊峰, 李飞. 基于WebGIS的神华机车实时状态远程监测系统[J]. 机车电传动, 2019(1): 117-120.

ZHANG Junfeng, LI Fei. Remote real-time status monitoring system for Shenhua locomotive based on WebGIS[J]. Electric Drive for Locomotives, 2019(1): 117-120.

刘菊香. 列车卫星定位故障影响分析与检测方法研究[J]. 铁道通信信号, 2022, 58(3): 1-6.

LIU Juxiang. Impact analysis of GNSS-based train location fault and study on its detection[J]. Railway Signalling & Communication, 2022, 58(3): 1-6.

邓浩江. 一种基于概率统计的列车定位算法[J]. 铁路通信信号工程技术, 2022, 19(4): 28-31.

DENG Haojiang. Train positioning algorithm based on probability statistics[J]. Railway Signalling & Communication Engineering, 2022, 19(4): 28-31.

罗淼, 党建武, 郝占军, 等. 基于卷积神经网络的列车位置指纹定位算法研究[J]. 铁道学报, 2023, 45(1): 42-50.

LUO Miao, DANG Jianwu, HAO Zhanjun, et al. Study of train location fingerprint positioning algorithm based on convolutional neural network[J]. Journal of the China Railway Society, 2023, 45(1): 42-50.

周政. BP神经网络的发展现状综述[J]. 山西电子技术, 2008(2): 90-92.

ZHOU Zheng. Survey of current progress in BP neural network[J]. Shanxi Electronic Technology, 2008(2): 90-92.

YANG Sibo, TING T O, MAN K L, et al. Investigation of neural networks for function approximation[J]. Procedia Computer Science, 2013, 17: 586-594.

PANG Zhonghua, LIU Guoping. Design and implementation of secure networked predictive control systems under deception attacks[J]. IEEE Transactions on Control Systems Technology, 2012, 20(5): 1334-1342.

YANG Jun, ZHOU Chunjie, YANG Shuanghua, et al. Anomaly detection based on zone partition for security protection of industrial cyber-physical systems[J]. IEEE Transactions on Industrial Electronics, 2018, 65(5): 4257-4267.

李常贤, 赵焱, 张彤, 等. 列车网络控制系统时延特性研究[J]. 中国铁路, 2018(1): 49-53.

LI Changxian, ZHAO Yan, ZHANG Tong, et al. Study on time delay characteristics of train network control system[J]. China Railway, 2018(1): 49-53.

乔红刚, 李习桥, 刘玉林. 运营动车组发生车体抖动问题的研究[J]. 铁道车辆, 2017, 55(7): 38-40.

QIAO Honggang, LI Xiqiao, LIU Yulin. Research on shaking of carbodies of multiple units in operation[J]. Rolling Stock, 2017, 55(7): 38-40.

陶汉卿, 蔡煊, 吴昕慧. 基于GPS和里程计的列车定位方法[J]. 城市轨道交通研究, 2017, 20(8): 144-149.

TAO Hanqing, CAI Xuan, WU Xinhui. Train positioning method based on GPS and ODO[J]. Urban Mass Transit, 2017, 20(8): 144-149.

吴雪, 李莹, 高磊. 高速列车内装系统基准定位控制技术[J]. 机车车辆工艺, 2013(5): 23-24.

WU Xue, LI Ying, GAO Lei. Reference positioning control technology for high-speed train interior decoration system[J]. Locomotive & Rolling Stock Technology, 2013(5): 23-24.

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