基于改进模糊PID-Smith控制器的高速动车组停车方法

李中奇; 许健

doi:10.19818/j.cnki.1671-1637.2020.04.011

基于改进模糊PID-Smith控制器的高速动车组停车方法

doi: 10.19818/j.cnki.1671-1637.2020.04.011

李中奇^{1, 2,},
许健^{1, 2}

1.
华东交通大学江西省先进控制与优化重点实验室，江西南昌 330013
2.
华东交通大学电气与自动化工程学院，江西南昌 330013

基金项目:

国家自然科学基金项目 51565012

国家自然科学基金项目 61673172

国家自然科学基金项目 61991404

江西省自然科学基金项目 20192BAB207026

详细信息

作者简介:
李中奇(1975-), 男, 黑龙江哈尔滨人, 华东交通大学教授, 工学博士, 从事轨道交通自动化与运行优化研究

中图分类号: U266.2
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出版历程
- 收稿日期: 2020-04-12
- 刊出日期: 2020-04-25

High-speed EMU parking method based on improved fuzzy PID-Smith controller

LI Zhong-qi^{1, 2
,},
XU Jian^{1, 2}

1.
Key Laboratory of Advanced Control and Optimization of Jiangxi Province, East China Jiaotong University, Nanchang 330013, Jiangxi, China
2.
School of Electrical and Automation Engineering, East China Jiaotong University, Nanchang 330013, Jiangxi, China

Funds:

National Natural Science Foundation of China 51565012

National Natural Science Foundation of China 61673172

National Natural Science Foundation of China 61991404

Natural Science Foundation of Jiangxi Province 20192BAB207026

More Information

Author Bio:
LI Zhong-qi(1975-), male, professor, PhD, lzq0828@163.com

摘要

摘要: 为解决动车组制动过程中电制动与空气制动切换时控制模型参数变化和空气制动延时大的问题, 以提高动车组停车的精确性, 提出了一种改进模糊PID-Smith控制器; 通过分析动车组制动过程中单个车厢的力学模型, 考虑列车制动过程的特点, 建立了关于运行速度和制动力的二阶纯延时传递函数; 将离散化的二阶纯延时传递函数与单个车厢的力学模型结合, 建立了动车组多质点控制模型, 并分析了该控制模型的特点; 提出了一种改进的模糊PID-Smith控制器, 通过引入Smith预估控制器解决了动车组制动过程中空气制动系统延时大的问题, 使用递推最小二乘法在线辨识了模型参数, 以解决动车组制动过程中电制动切换到空气制动时的模型参数变化问题; 采用模糊PID控制器代替Smith预估控制器中的PID部分, 解决了PID参数整定难和鲁棒性差的问题; 采用MATLAB软件对CRH380A型高速动车组进行仿真, 在不同进站速度、不同减速度和不同程度干扰下, 使控制器控制动车组跟踪设定速度, 并与模糊PID控制器的结果进行对比。仿真结果表明: 改进模糊PID-Smith控制器得到的动力单元速度与其设定速度的误差在0.4 km·h^-1以内, 而模糊PID控制器的误差在1.0 km·h^-1以内; 采用提出控制器得到的停车误差在0.3 m以内, 而模糊PID控制器的停车误差在1.5 m以内; 提出的控制器满足高速动车组运行过程中停车误差小于0.3 m的要求。
- 动车组 /
- 制动 /
- 多质点 /
- 模糊PID /
- Smith预估控制器
Abstract: To solve the problems of control model parameter change when the electric brake and air brake were switched in the EMU braking process and the large delay of air brake system, and improve the parking accuracy of EMU, an improved fuzzy PID-Smith controller was proposed. A second-order pure delay transfer function with respect to the running speed and braking force was established by analyzing the mechanical model of a single carriage in the braking process of EMU and considering the characteristics of braking process of trains. The discretized second-order pure delay transfer function was combined with the mechanical model of a single carriage to establish the multi-particle control model of EMU, and the characteristics of this control model were analyzed. An improved fuzzy PID-Smith controller was proposed. The Smith predicting controller was introduced to solve the large delay problem of air brake system during the braking process of EMU. The recursive least square method was used to identify the model parameters online to solve the problem of model parameter change when the electric brake switching to the air brake during the braking process of EMU. The fuzzy PID controller was used to replace the PID part of Smith predicting controller to solve the problems of PID parameter tuning difficulty and poor robustness. The software MATLAB was used to simulate the CRH380 A high-speed EMU. The controller was used to control the EMU to track the set speed under the conditions of different inbound speeds, different decelerations and different degrees of interferences, and the results were compared with those of fuzzy PID controller. Simulation result shows that the errors between the power unit speed and the set speed obtained by the improved fuzzy PID-Smith controller and fuzzy PID controller are within 0.4 and 1.0 km·h^-1, respectively. The parking error obtained by the proposed controller and fuzzy PID controller are within 0.3 and 1.5 m, respectively. Therefore, the proposed controller satisfies the requirement that the parking error during the operation of high-speed EMU should be less than 0.3 m.
- EMU /
- braking /
- multi-particle /
- fuzzy PID /
- Smith predicting controller

HTML全文

图 1 CRH380A型动车组单节车厢受力分析

Figure 1. Force analysis on single carriage of CRH380A EMU

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图 2 停车时电制动力和空气制动力的变化过程

Figure 2. Change processes of electric brake and air brake forces during parking

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图 3 Smith预估控制器结构

Figure 3. Structure of Smith predicting controller

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图 4 模糊PID控制器的基本原理

Figure 4. Basic principle of fuzzy PID controller

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图 5 改进模糊PID-Smith控制器原理

Figure 5. Principle of improved fuzzy PID-Smith controller

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图 6 改进模糊PID-Smith控制器和模糊PID控制器的仿真结果对比

Figure 6. Comparison of simulation results between improved fuzzy PID-Smith controller and fuzzy PID controller

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图 7 进站速度为40 km·h^-1时改进模糊PID-Smith控制器和模糊PID控制器的停车误差曲线

Figure 7. Parking error curves of improved fuzzy PID-Smith controller and fuzzy PID controller when inbound speed is 40 km·h^-1

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图 8 进站速度为33 km·h^-1时改进模糊PID-Smith控制器和模糊PID控制器的停车误差曲线

Figure 8. Parking error curves of improved fuzzy PID-Smith controller and fuzzy PID controller when inbound speed is 33 km·h^-1

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图 9 进站速度为24 km·h^-1时改进模糊PID-Smith控制器和模糊PID控制器的停车误差曲线

Figure 9. Parking error curves of improved simulation fuzzy PID-Smith controller and fuzzy PID controller when inbound speed is 24 km·h^-1

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