报告详情
Self-Learning Optimal Control of Maglev Levitation Systems with Track Irregularity and Speed Constraints: A Reinforcement Learning driven Method for Parameters Adjustment
编号：27
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更新：2025-10-11 22:16:19 浏览：5次
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摘要
The maglev train achieves frictionless stable levitation. Nevertheless, track irregularities and high speed lead to fluctuations in the levitation gap. Thus, the control parameters of the levitation system must be adjusted to ensure safe operation. At present, the parameters’ adjustment mainly relies on expert experience and not adapted to dynamic changes. Therefore, this study proposes a reinforcement learning driven method for adjustment of the levitation system control parameters. Firstly, the levitation model considering speed and track irregularities is established. Secondly, a reinforcement learning driven control parameter adjustment method is presented. The control parameters are modified in real-time. Finally, simulation verification is conducted. Three typical speed scenarios are designed to test the levitation system over irregular tracks. The results indicate that after adjustment the levitation gap fluctuations are significantly reduced. Moreover, the control performance evaluation indicators also performed exceptionally well. The method is of great significance for ensuring the stable operation of maglev trains across the entire speed range.
关键词
Maglev trains, levitation system, reinforcement learning, parameters adjustment, track irregularities.
报告人
Mingda Zhai
Mr National University of Defense Technology稿件作者
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重要日期
-
会议日期
11月07日
2025
至11月09日
2025
-
10月12日 2025
初稿截稿日期
-
10月20日 2025
注册截止日期
主办单位
IEEE西南交通大学IAS学生分会
承办单位
西南交通大学电气工程学院
SPACI车网关系研究室
四川大学电力系统稳定与高压直流输电研究团队
SPACI车网关系研究室
四川大学电力系统稳定与高压直流输电研究团队
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