基于改进滑模极值搜索算法的货运列车黏着控制研究

程翔; 唐润忠; 黄刚; 黄宜山

doi:10.13890/j.issn.1000-128X.2023.01.014

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基于改进滑模极值搜索算法的货运列车黏着控制研究

电力牵引与控制 | 更新时间：2024-08-02

- 基于改进滑模极值搜索算法的货运列车黏着控制研究
- Adhesion control research for freight trains based on an improved sliding mode extremum seeking algorithm
- 机车电传动 2023年第1期页码：104-112
- 作者机构：
  
  1.湖南铁道职业技术学院轨道交通智能控制学院，湖南株洲 412001
  2.湖南工业大学电气与信息工程学院，湖南株洲 412007
  3.湖南工业大学轨道交通学院，湖南株洲 412007
  4.中车时代电动汽车股份有限公司，湖南株洲;412007
- 作者简介：
  
  程翔（1991—），男，工程师，主要从事铁道车辆的黏着控制、智能参数辨识的研究工作；E-mail: chengxiang@petalmail.com
- 基金信息：
  
  国家自然科学基金项目(62173137);湖南省教育厅科学研究项目(21C1295)
- DOI：10.13890/j.issn.1000-128X.2023.01.014
  中图分类号： U292.92;U260.11⁺5
- 纸质出版日期：2023-01-10，
  
  收稿日期：2022-02-27，
  
  修回日期：2023-01-01，
- 稿件说明：
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程翔, 唐润忠, 黄刚, 等. 基于改进滑模极值搜索算法的货运列车黏着控制研究[J]. 机车电传动, 2023(1): 104-112.

CHENG Xiang, TANG Runzhong, HUANG Gang, et al. Adhesion control research for freight trains based on an improved sliding mode extremum seeking algorithm[J]. Electric Drive for Locomotives, 2023(1): 104-112.
程翔, 唐润忠, 黄刚, 等. 基于改进滑模极值搜索算法的货运列车黏着控制研究[J]. 机车电传动, 2023(1): 104-112. DOI： 10.13890/j.issn.1000-128X.2023.01.014.

CHENG Xiang, TANG Runzhong, HUANG Gang, et al. Adhesion control research for freight trains based on an improved sliding mode extremum seeking algorithm[J]. Electric Drive for Locomotives, 2023(1): 104-112. DOI： 10.13890/j.issn.1000-128X.2023.01.014.

摘要

针对传统的滑模极值搜索控制算法（SMESC）在货运列车最佳黏着工作点追踪过程中存在稳态振荡、收敛速度慢的问题，提出了参数时变的改进滑模极值搜索控制算法。为削弱稳态振荡和加快SMESC收敛速度，分析了SMESC中增益参数与稳态振幅间的数学关系，以及辅助函数斜率与收敛性的联系，并对其进行优化：设计了时变辅助函数斜率以改善SMESC的收敛速度，设计了以观测误差为基准的动态增益参数以削减稳态振荡，并进行了收敛性分析。针对行车阻力无法直接测量的问题，引入了基于粒子群算法（PSO）的阻力参数估计，最后设计了基于SMESC的黏着控制律，通过与传统滑模极值搜索进行对比，证实了所提方法的有效性和实用性。

Abstract

This paper presented an improved sliding mode extremum seeking control (SMESC) algorithm with time-varying parameters

as a solution to steady-state oscillation and slow convergence speed of the traditional SMESC algorithm in tracking the optimal adhesion point of freight trains. In order to weaken the steady-state oscillation and speed up the convergence speed of SMESC

the mathematical relationship between the gain parameter and steady-state amplitude in SMESC and the correlation between the slope of auxiliary function and convergence were analyzed and optimized. The time-varying slope of auxiliary function was designed to improve the convergence speed of SMESC

and a dynamic gain parameter based on observation error was designed to reduce steady-state oscillation

and the convergence was analyzed. A resistance parameters estimation method based on particle swarm algorithm (PSO) was proposed to deal with the absence of running resistance due to inaccessibility for measurement. At last

the SMESC-based adhesion control law was designed

and the effectiveness and practicability of the proposed method were verified by comparing with the traditional SMESC algorithm.

关键词

黏着控制极值搜索观测器稳态振荡

Keywords

adhesion controlextremum seekingobserversteady-state oscillation

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