针对既有线路自动控制系统实时性和准确性不足等问题,提出一种列控系统智能化升级方案.首先,针对列车运行中电制动和空气制动阶段的独特性,考虑电制动和空气制动间的切换,分别建立针对电制动和空气制动的精确制动模型.然后,对列车自动运行系统(automatic train operation,ATO)控制器进行优化,应用滑模自适应鲁棒控制策略进行实时调整,增强控制器鲁棒性,以适应不同车辆参数和外部环境干扰.最后,以北京地铁5号线为例,对列车精确制动模型和滑模自适应鲁棒控制器进行仿真验证,计算列车停车精度与速度误差.研究结果表明:与比例积分微分(proportional integral derivative,PID)控制和滑模控制算法相比,利用本文提出的控制算法计算得到的停车精度和速度误差更小,停车精度均值达8 cm以下.
Key Issues in Real-time Optimization of Braking Performance for Urban Rail Transit Trains
To address the shortage of real-time data and the accuracy limitations of existing automatic line control systems,we propose an intelligent upgrade scheme for train control systems.First,considering the distinct characteristics of electric and air braking in train operations,we developed accurate braking models for both systems,incorporating the switch between electric and air braking.Next,we optimized the ATO controller and applied a sliding mode adaptive robust control strategy.This strategy adjusts the controller in real time,enhancing its robustness and adaptability to varying vehicle parameters and external environmental interferences.Using Beijing Metro Line 5 as a case study,we simulated the precise train braking model and the sliding mode adaptive robust controller to calculate the stopping accuracy and speed error of the train.The results demonstrate that,compared to proportional-integral-derivative(PID)control and sliding mode control,the proposed control algorithm significantly reduces parking accuracy and speed errors.Specifically,the average parking accuracy achieved is less than 8 cm.
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