高速动车组强耦合模型的分布式滑模控制策略  

Distributed Sliding Mode Control Strategy for High-speed EMU Strong Coupling Model

作　　者：李中奇 金柏 杨辉 谭畅 付雅婷 LI Zhong-Qi;JIN Bai;YANG Hui;TAN Chang;FU Ya-Ting(School of Electrical and Automation Engineering,East China Jiaotong University,Nanchang 330013;Key Laboratory of Advanced Control and Optimization of Jiangxi Province,Nanchang 330013)

机构地区：[1]华东交通大学电气与自动化工程学院,南昌330013 [2]江西省先进控制与优化重点实验室,南昌330013

出　　处：《自动化学报》2020年第3期495-508,共14页Acta Automatica Sinica

基　　金：国家自然科学基金(51565012,61673172,61663013,61803155)资助。

摘　　要：高速动车组是由多节车辆与钩缓装置链接而成的复杂系统.将钩缓装置等效成弹簧?阻尼器系统,分析动车组运行过程中钩缓装置对相邻车辆作用的动力学机理,明确作用方式,建立高速动车组的强耦合模型.根据列车模型动力或制动力输入的分散特征,设计分布式神经网络滑模控制策略,对高速动车组进行速度跟踪控制.为减小速度跟踪过程中未知因素对高速动车组控制精度的影响,利用列车历史运行数据,采用历史工况数据中心对当前控制律输出进行补偿以提高控制精度与实用稳定性.采用高速动车组运行仿真平台的仿真实验结果表明,该建模方法较以往多质点模型更能体现高速动车组运行特性,且采用补偿规则的控制策略优于传统控制效果.The high-speed EMU(electric multiple units)is a complex system composed of multi-section vehicles and hooking devices.This paper compares the hooking device into a spring-damper system,and analyzes the dynamic mechanism and mode of the action of coupler and buffer device on adjacent vehicles during the operation of high-speed EMU,and then,establishes the strong coupling model of high-speed EMU.According to the decentralized characteristics of the input of train model power or braking force,a distributed neural network sliding mode control strategy is designed to track the speed of high-speed EMU.In order to reduce the influence of unknown factors on the control accuracy of high-speed EMU during speed tracking,using the historical train operating data,the historical operating data center is used to compensate the current control law to improve control accuracy and practical stability.The simulation results of the high-speed EMU Operation Simulation Platform show that the modeling method can better reflect the operation characteristics of the high-speed EMU than the previous multi-point model,and the control strategy with compensation rules is better than the traditional control effect.

关 键 词：高速动车组 强耦合模型 分布式神经网络滑模控制 跟踪控制 数据补偿 

分 类 号：U266[机械工程—车辆工程] TP183[交通运输工程—载运工具运用工程]