基于多种群遗传算法的磁浮列车自抗扰速度控制

李自康; 戴春辉; 黄翠翠; 龙志强

doi:10.3969/j.issn.0258-2724.20240113

基于多种群遗传算法的磁浮列车自抗扰速度控制

doi: 10.3969/j.issn.0258-2724.20240113

国防科技大学智能科学学院，湖南长沙 410000

基金项目: 国家自然科学基金项目（52332011）

详细信息

作者简介:
李自康（1995—），男，博士研究生，研究方向为电磁悬浮与推进控制，E-mail：lizikang24@nudt.edu.cn

通讯作者:
戴春辉（1984—），男，副研究员，博士，研究方向为电磁悬浮与推进控制、智能诊断与容错控制，E-mail：daichunhui@nudt.edu.cn

中图分类号: U238
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- 被引次数: 0
出版历程
- 收稿日期: 2024-03-07
- 修回日期: 2024-08-25
- 网络出版日期: 2025-03-05

Active Disturbance Rejection Speed Control for Maglev Trains Based on Multiple Population Genetic Algorithm

College of Intelligence Science and Technology, National University of Defense Technology, Changsha 410000, China

摘要

摘要:
相较传统轮轨交通方式，磁浮列车具有速度快、噪音小、平稳性高、维护成本低等不可替代的优势，为构建便捷顺畅的城市交通系统提供一种理想方案. 为实现磁浮列车在复杂扰动环境下的精准速度运行控制，提出一种参数自整定的自抗扰控制(ADRC)方法. 首先，通过受力分析建立磁浮列车纵向动力学模型，用于描述运行控制过程中的非线性迟滞特性；将模型不确定参数及外部扰动等因素归纳为扩张状态，设计三阶扩张状态观测器实时观测扩张状态，并基于李雅普诺夫稳定性定理对观测器的收敛性条件进行分析；针对传统ADRC控制参数多、调参困难的问题，引入多种群遗传算法(MPGA)实现参数自适应优化和调整；最后，利用磁浮列车现场运行采集的数据开展仿真实验，结果表明：相较传统ADRC，MPGA-ADRC在速度控制精度方面提升22.7%，跟踪平稳性提升25.6%，表明所提出的方法能够有效提升磁浮列车运行的稳定性和乘坐舒适性.
- 磁浮列车 /
- 自抗扰控制 /
- 扩张状态观测器 /
- 多种群遗传算法 /
- 速度控制
Abstract:
Compared with traditional wheel-rail transportation, maglev trains have some irreplaceable advantages such as high speed, low noise, smooth operation, and low maintenance cost, providing an ideal scheme for constructing a convenient urban transportation system. To realize precise speed control of maglev trains in complex disturbance environments, an active disturbance rejection control (ADRC) method with self-tuning parameters was proposed. Firstly, the longitudinal dynamic model of maglev trains was established by force analysis to describe the nonlinear hysteresis characteristics of maglev trains during operation. Secondly, the unknown parameters of the model and external disturbances were regarded as the extended state, and a third-order extended state observer was designed to observe the extended state in real time. In addition, the convergence condition of the observer was analyzed based on the Lyapunov stability theorem. Then, to solve the problem of many control parameters and difficult parameter adjustment in traditional ADRC, the multiple population genetic algorithm (MPGA) was introduced to realize adaptive optimization and adjustment of parameters. Finally, the simulation experiment was carried out based on the data collected from the real operation environment of maglev trains, and the simulation results show that compared with traditional ADRC, the speed control accuracy is increased by 22.7% and the tracking stability is improved by 25.6% by means of MPGA-ADRC method, which indicates that the proposed method can effectively improve the stability and ride comfort of maglev trains.
- maglev train /
- active disturbance rejection control /
- extended state observer /
- MPGA /
- speed control

HTML全文

图 1 磁浮列车MPGA-ADRC控制框架

Figure 1. MPGA-ADRC frame of maglev train

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图 2 MPGA算法原理

Figure 2. Principle of MPGA

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图 3 适应度变化过程

Figure 3. Change process of fitness

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图 4 参数调整过程

Figure 4. Process of parameters’ adjustment

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图 5 基于MPGA-ADRC的磁浮列车速度控制结构

Figure 5. Speed control structure of maglev train based on MPGA-ADRC

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图 6 唐山线固定限速曲线及ATO跟踪曲线

Figure 6. Fixed speed limit curve and ATO tracking curve of Tangshan Line

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图 7 对唐山线的跟踪效果

Figure 7. Tracking effect of Tangshan Line

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图 8 速度跟踪误差

Figure 8. Speed tracking errors

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图 9 加速度误差

Figure 9. Acceleration errors

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图 10 ESO观测效果

Figure 10. Observation effect of ESO

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图 11 ESO观测误差

Figure 11. Observation errors of ESO

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表 1 算法效果对比

Table 1. Comparison of algorithm effects

运行次数	MPGA		SGA
运行次数	迭代次数/次	最优值	迭代次数/次	最优值
第1次	27	0.0776	50	0.0769
第2次	25	0.0776	50	0.0776
第3次	21	0.0776	50	0.0764

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表 2 MPGA-ADRC控制参数

Table 2. Control parameters of MPGA-ADRC

名称	控制参数
TD	$ \delta = 0.9 $，$ h = 0.01 $，$ {h_0} = 0.011 $
ESO	$ \alpha = 0.5 $，$ \tau = 0.01 $，$ {\beta _1} = 1\;009.3 $， $ {\beta _2} = 1\;559.7 $，$ {\beta _3} = 4\;397.3 $
NLSEF	$ {k_{\text{p}}} = 17.3 $，$ {k_{\text{d}}} = 14.8 $

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表 3 控制器性能比较

Table 3. Comparison of controllers performance

名称	最大速度误差（m·s⁻¹）	速度RMSE（m·s⁻¹）	加速度RMSE（m·s⁻²）
ADRC	0.4841	0.1468	0.1320
GA-ADRC	0.4211	0.1294	0.1135
MPGA-ADRC	0.3109	0.1135	0.0982

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