基于制动特征自学习的磁浮列车强化学习制动控制

刘鸿恩; 胡闽胜; 胡海林

doi:10.3969/j.issn.0258-2724.20230517

基于制动特征自学习的磁浮列车强化学习制动控制

doi: 10.3969/j.issn.0258-2724.20230517

刘鸿恩^{1, 2, 3,},
胡闽胜^{1, 2},
胡海林^{1, 2, ,}

1.
江西理工大学永磁磁浮技术与轨道交通研究院，江西赣州 341000
2.
江西省磁悬浮技术重点实验室，江西赣州 341000
3.
北京全路通信信号研究设计院集团有限公司，北京 100073

基金项目: 国家自然科学基金（52262050）；江西省自然科学基金（20224BAB202025）

详细信息

作者简介:
刘鸿恩（1987—），男，讲师，博士，研究方向为轨道交通系统建模与优化控制， E-mail：len3610@126.com

通讯作者:
胡海林（1984—），男，副教授，博士，研究方向为磁浮列车牵引驱动系统控制策略， E-mail：huhailin@jxust.Edu.cn

中图分类号: U283.1
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- 文章访问数: 30
- HTML全文浏览量: 7
- PDF下载量: 10
- 被引次数: 0
出版历程
- 收稿日期: 2023-10-09
- 修回日期: 2024-02-16
- 网络出版日期: 2024-04-20

Reinforcement Learning Braking Control of Maglev Trains Based on Self-Learning of Hybrid Braking Features

LIU Hongen^{1, 2, 3
,},
HU Minsheng^{1, 2},
HU Hailin^{1, 2
, ,}

1.
Institute of Permanent Magnetic Levitation Technology and Rail Transportation, Jiangxi University of Science and Technology, Ganzhou 341000, China
2.
Key Laboratory of Maglev Technology of Jiangxi Province, Ganzhou 341000, China
3.
CRSC Research & Design Institute Group Co., Ltd., Beijing 100073, China

摘要

摘要:
精准、平稳停车是磁浮列车自动驾驶制动控制的重要目标. 中低速磁浮列车停站制动过程受到电-液混合制动状态强耦合等影响，鉴于制动特征理论模型的传统制动控制方法难以保障磁浮列车的停车精度和舒适性. 本文提出一种基于混合制动特征自学习的磁浮列车强化学习制动控制方法. 首先，采用长短期记忆网络建立磁浮列车混合制动特征模型，结合磁浮列车运行环境和状态数据进行动态制动特征自学习；然后，根据动态特征学习结果更新强化学习的奖励函数与学习策略，提出基于深度强化学习的列车制动优化控制方法；最后，采用中低速磁浮列车现场运行数据开展仿真实验. 实验结果表明：本文所提出制动控制方法较传统方法的舒适性和停车精度分别提高41.18%和22%，证明了本文建模与制动优化控制方法的有效性.
- 磁浮列车 /
- 制动控制 /
- 多目标优化控制 /
- 强化学习 /
- 制动特征自学习
Abstract:
Accurate and smooth parking is an essential goal for automatic driving braking control of maglev trains. The strong coupling of the electro-hydraulic hybrid braking state affects the medium and low-speed maglev trains during the stopping braking process, and the traditional braking control method based on the theoretical model of braking features makes it difficult to guarantee the parking accuracy and comfort of the maglev train. This paper proposed a reinforcement learning braking control method for maglev trains based on self-learning of hybrid braking features. First, a long short-term memory (LSTM) network was used to establish a hybrid braking feature model for maglev trains, and the self-learning of dynamic braking features was performed based on the operating environment and status data of maglev trains. Then, the reward function and learning strategy of reinforcement learning were updated according to the learning results of dynamic features, and a train braking optimization control method based on deep reinforcement learning was proposed. Finally, simulation experiments were carried out by using on-site operation data of medium and low-speed maglev trains. The experimental results show that the braking control method proposed in this paper improves comfort and parking accuracy by 41.18% and 22%, respectively, compared with the traditional method. It proves the effectiveness of the modeling and braking optimization control method in this paper.
- maglev train /
- braking control /
- multi-objective optimal control /
- reinforcement learning /
- self-learning of braking features

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图 1 中低速磁浮列车电-液混合制动控制原理

Figure 1. Principle of electro-hydraulic hybrid braking control of medium and low-speed maglev trains

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图 2 磁浮列车混合制动动态特性

Figure 2. Dynamic features of hybrid braking of maglev train

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图 3 基于LSTM的磁浮列车制动模型

Figure 3. Braking model of maglev train based on LSTM

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图 4 BFS-DQN框图

Figure 4. Framework of BFS-DQN

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图 5 强化学习制动优化控制算法流程图

Figure 5. Flow chart of optimization control algorithm for reinforcement learning braking

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图 6 奖励函数变化曲线

Figure 6. Change curve of reward function

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图 7 加速度收敛情况

Figure 7. Acceleration convergence condition

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图 8 停车误差收敛情况（DQN和BFS-DQN分别取停车误差的绝对值和绝对值负值）

Figure 8. Convergence of parking errors （DQN and BFS-DQN take the absolute value and negative value of absolute value of parking error, respectively）

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图 9 制动曲线对比图

Figure 9. Braking curve comparison

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图 10 制动等级和加速度变化情况

Figure 10. Braking rating and acceleration changes

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图 11 停车误差对比

Figure 11. Comparison of parking errors

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表 1 仿真列车参数

Table 1. Simulation train parameters

参数类别	参数特性
列车质量/t	75
线路最高限速/（km·h⁻¹）	80
编组数量	3
最大常用制动力/kN	74.23
最大常用减速度/（m·s⁻²）	0.96
线路最大坡度/‰	51.01

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表 2 算法主要训练参数

Table 2. Algorithm training parameters

参数	BFS-DQN	DQN
LSTM迭代次数/次	500	—
LSTM学习率	0.001	—
LSTM样本批量	50	—
单次训练最大步数	80	80
训练最大次数/次	20000	20000
Q网络学习率	0.001	0.001
Q网络更新频率	100	100
样本大小	32	32
经验池容量	2000	2000
折扣因子	0.96	0.96
贪婪率初始值	0.9	0.9
贪婪率最终值	0.1	0.1

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表 3 算法训练结果

Table 3. Algorithm training results

训练结果	BFS-DQN	DQN
平均奖励值	33.5	27.8
平均状态转移次数/次	70	72
平均停车误差/m	0.10	0.15
平均加速度变化/（cm·s⁻³）	10.84	11.78
平均制动时间/s	14.0	14.4

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表 4 算法性能

Table 4. Algorithm performance

制动控制策略	RMSE	SD
BFS-DQN	0.099048	0.070652
DQN	0.142815	0.110446
传统ATO	0.276103	0.140018

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表 5 停车误差分布情况

Table 5. Distribution of parking errors 次

停车误差/m	BFS-DQN	DQN	ATO
$ x \lt - 0.5 $	0	0	3
$ - 0.5 \leqslant x \leqslant - 0.3 $	0	2	3
$ - 0.3 \lt x \leqslant 0 $	18	19	16
$0 \lt x \leqslant 0.3$	32	29	20
$ 0.3 \lt x \leqslant 0.5 $	0	0	6
$ x \gt 0.5 $	0	0	2

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