动车组进站过程精准停车控制方法研究  

Research on Precision Parking Control Method for EMU Inbound Process

作　　者：李中奇 邢月霜 Li Zhongqi;Xing Yueshuang(Key Laboratory of Advanced Control&Optimization of Jiangxi Province,Nanchang 330013,China;School Electrical and Automation Engineering,East China Jiaotong University,Nanchang 330013,China)

机构地区：[1]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

出　　处：《系统仿真学报》2021年第1期149-158,共10页Journal of System Simulation

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

摘　　要：通过分析高速动车组进站停车过程中制动力与速度之间的关系,构建动车组多质点动力学模型。由于制动停车后期产生空气制动力过程给系统带来延时影响,引入Smith预估器,采用基于RBF(Radial Basis Function)神经网络的PID控制策略与Smith预估器相结合,实现制动过程中对给定速度的跟踪控制。仿真分析表明:该混合控制器控制的列车速度与设定速度的误差小于±1 km/h,停车误差小于±0.3 m,可满足进站制动停车的要求。By analyzing the relationship between braking force and speed during the stop-stop process of high-speed EMU(Electric Multiple Units), the multi-point dynamics model of EMU is constructed. The Smith predictor is introduced due to the delay effect on the system caused by the braking force generated during the late braking, and the RBF(Radial Basis Function) neural network-based PID control strategy is combined with the Smith predictor to achieve tracking control of a given speed during braking. Simulation analysis shows that the error of train speed and set speed controlled by RBF neural network PID-Smith controller is less than ±1 km/h, and the parking error is less than ±0.3 m, which meets the inbound braking and parking requirement.

关 键 词：动车组 多质点建模 RBF神经网络 自适应PID控制 预估补偿控制 

分 类 号：TP273[自动化与计算机技术—检测技术与自动化装置]