基于非线性补偿的轨道交通列车自定位方法研究

韩芝星

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

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基于非线性补偿的轨道交通列车自定位方法研究

电力牵引与控制 | 更新时间：2024-08-02

- 基于非线性补偿的轨道交通列车自定位方法研究
- Nonlinear compensation-based self-positioning method for rail transit train
- 机车电传动 2023年第2期页码：131-135
- 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：
  
  韩芝星（1987—），女，硕士，讲师，从事智能仪表与自动化装置研究；E-mail: hanzhixing0721@163.com
- 基金信息：
  
  2022年度山西省高等学校科技创新项目(2022L692)
- DOI：10.13890/j.issn.1000-128X.2023.02.015
  中图分类号： U2;U228.1
- 纸质出版日期：2023-03-10，
  
  收稿日期：2021-05-30，
  
  修回日期：2023-01-16，
- 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

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