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Optimal Traction Control for Heavy-haul Train Using Dynamic Response Identification Model
编号：127 访问权限：仅限参会人 更新：2023-11-20 13:53:19 浏览：233次 张贴报告

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摘要
The Automatic Train Operation (ATO) equipment is specifically designed to ensure optimal traction for heavy-haul trains operating under challenging conditions. Onboard controllers are installed on locomotives to enhance freight transportation capacity, reduce costs, and alleviate driver workload. These controllers can be engaged or disengaged to switch between two distinct operating modes: the inertial response mode characterized by a longer time delay, and the open mode which offers better anti-interference capability. This paper proposes an optimal traction control strategy for heavy-haul trains utilizing dynamic response identification technology. The strategy compares three ATO control algorithms based on the same identified vehicle model that considers time delay; however, each algorithm relies on output signals from different sensors. By simulating with real section data from the "Shenchi-Nan-Ningwuxi" heavy haul railway of "Shuohuang," this paper verifies the control effect of the proposed optimal traction control strategy for heavy haul trains in the presence of disturbance. Simulation results based on real line data and HXD1 electric locomotive running data demonstrate significant improvement in speed tracking ability achieved through our approach.
 
关键词
Heavy-haul trains, Dynamic response identification, Automatic train operation, Variable universe fuzzy PID-Smith controller, Disturbance observer
报告人
zhang kunpeng
Lecturer East China Jiaotong University

稿件作者
zhang kunpeng East China Jiaotong University
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重要日期

  • 会议日期

    12月08日

    2023

    至

    12月10日

    2023

  • 11月01日 2023

    初稿截稿日期

  • 12月10日 2023

    注册截止日期

主办单位
IEEE IAS
承办单位
Southwest Jiaotong University (SWJTU)
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