基于二维Roesser模型与范数优化迭代学习控制的市域铁路列车速度追踪研究

City Railway Train Speed Tracking Based on Two-dimensional Roesser Model and Norm Optimal Iterative Learning Control

ATO(列车自动运行)技术通过控制算法实现列车运行速度自动调节,通过对控制算法进行优化,可使列车运行速度更加贴合目标速度。轨道交通列车作为具有高度重复性的非线性动力学系统,在固定线路的时间轴和批次轴具有二维动态演化的特性,因此考虑将二维模型迭代学习控制应用于轨道交通列车。不同车厢在列车运行时所受空气阻力不同,将列车车厢分为头、中、尾三类,求解基于多质点模型的列车动力学方程,对其进行线性化处理,得到列车的状态空间方程;应用二维系统理论,将其转换为二维Roesser模型;设计范数优化迭代学习控制算法,将其作为二维模型的输入信号,验证了新建立的二维模型具有渐进稳定性。同时,选择市域铁路D型列车组作为研究对象,通过对速度、位移追踪仿真,验证了优化后算法的可行性;并选择了两种常用的一维系统ILC(迭代学习控制)进行速度追踪仿真,通过比较三者的追踪误差,验证了优化后算法的追踪性能优于其余两者。

ATO(automatic train operation)technology realizes automatic adjustment of train operating speed through control algorithms,and by optimizing the control algorithm,the train operating speed can be better matched to the target speed.As a highly repetitive nonlinear dynamical system,the rail transit train has the characteristics of two-dimensional dynamic evolution in the time axis and batch axis of a fixed line.Thererfore,the two-dimensional model ILC(iterative learning control)is considered for application to rail transit train.Different train compartments experience different air resistance during train operation,and the train compartments are divided into three categories:head,middle and tail.The train dynamics equation is solved based on a multi-particle model.The train state space equation is obtained by linearizing the equation,and the two-dimensional system theory is used to convert it into a two-dimensional Roesser model.The norm optimal ILC algorithm is designed as the input signal of the two-dimensional model,and the stability of the two-dimensional model is verified.The D-type train set of city railway is selected as the research object,feasibility of the optimized algorithm is verified through tracking simulation of speed and displacement.Two commonly used one-dimensional systems ILC are selected for speed tracking simulation.By comparing the tracking errors of the above three,the superiority of the optimized algorithm over the other two is verified.