高速列车自动驾驶前馈自适应广义预测控制方法研究  

Research on Feed Forward Adaptive-generalized Model Predictive Control for High-speed Train Automatic Operation

作　　者：罗源 易杰 白金磊 张征方 LUO Yuan;YI Jie;BAI Jinlei;ZHANG Zhengfang(Zhuzhou CRRC Time Electric Co.,Ltd.,Zhuzhou,Hunan 412001,China)

机构地区：[1]株洲中车时代电气股份有限公司,湖南株洲412001

出　　处：《控制与信息技术》2024年第1期14-22,共9页CONTROL AND INFORMATION TECHNOLOGY

基　　金：中国国家铁路集团有限公司科技研究开发计划课题(P2023J001)。

摘　　要：高速列车自动驾驶(ATO)系统本质上是强非线性和不确定性的系统,针对高速列车模型参数非线性和时变性等特点,文章提出了一种前馈自适应广义预测控制(FA-GPC)的方法对ATO系统进行动态优化控制,并设计了一种带约束的多目标预测控制器。首先,基于列车多质点模型,分析附加阻力的改变对列车运行的影响;然后,结合列车运行过程中的速度跟踪精度、停车精度及运行舒适性等关键指标,构建包含控制输入约束的多目标性能指标函数,设计基于多目标函数的前馈广义预测速度跟踪控制算法,解决了由于附加阻力变化导致控制器超调的问题并加快了控制收敛速度。由于列车运行过程中受外界环境、乘客流动等因素影响,阻力变化大,难以建立精确的数学模型,因此采用带约束的变遗忘因子递推最小二乘法来辨识出列车控制系统在不同工况下受控自回归积分滑动平均模型(CARIMA),进而提高控制系统的鲁棒性。仿真结果表明,相比传统的无前馈GPC和PID控制器,前馈广义控制器在不同线路条件下巡航控速速度跟踪精度在±0.5km/h范围内,具有良好的跟踪性;在强扰动情况下通过引入自适应改进的前馈广义预测控制算法,具有较强的鲁棒性。High-speed automatic train operation(ATO)systems inherently exhibit strong nonlinearity and uncertainty.In view of the characteristics of nonlinearity and time-variability of high-speed train model parameters,this paper proposes a feed forward adaptive-generalized model predictive control(FA-GPC)method for dynamic optimization control of the ATO system along with a constrained multi-object predictive controller.Based on a multi-particle train model,an initial analysis explores the impacts of additional resistance changes on train operation.Subsequently,a multi-object performance indicator function containing control input constraints is constructed,combined with key indicators during the operation of high-speed trains,such as speed tracking accuracy,stopping accuracy,and riding comfort.Furthermore,a feed forward generalized prediction speed tracking control algorithm is designed based on the multi-object function,aiming to solve controller overshoot due to additional resistance changes and enhance control convergence rates.Taking into account various factors including influences of external environments and passenger movement during train operation,the resistance changes greatly,making it difficult to establish an accurate mathematical model,a constrained variable forgetting factor-recursive least square method is incorporated to identify the controlled autoregressive integrated moving average model(CARIMA)of the train control system under different operational conditions.This approach aims to improve the robustness of the control system.Simulation results show that,compared with traditional GPC in the absence of the feed forward functionality and PID controller,the proposed feed forward generalized controller demonstrates good cruise control speed tracking accuracy within a±0.5 km/h range under different line conditions and strong robustness thanks to the adaptive modification to the feed forward generalized predictive control algorithm for better performance in strong disturbance conditions.

关 键 词：高速列车 自动驾驶 自适应广义预测控制 参数辨识 

分 类 号：V323.19[航空宇航科学与技术—人机与环境工程] U292.914[交通运输工程—交通运输规划与管理]