面向虚拟编组的高速列车鲁棒模型预测控制OA

To address the growing transport demands of high-speed railways and enhance the overall operational efficiency of the high-speed network,this study focused on the safe and efficient control of high-speed trains operating under a virtual formation mode.Taking the CRH380A high-speed train as the research subject,a virtual formation operation mode covering the entire phase from departure formation to disassembly upon arrival was proposed.To ensure adherence to this mode,a multi-train,multi-mass dynamics model was established,accounting for non-linear operational resistance during train movement,inter-carriage interactions,and external unknown disturbances.Based on this,an optimized operational objective function was designed incorporating constraints such as speed limitations,operational spacing restrictions,and control quantity limitations during high-speed train operations.Subsequently,a robust model predictive control method based on a hierarchical architecture was proposed.The solution layer employed a quadratic programming strategy to generate optimal reference trajectories for train group operations in real time,while the control layer calculates optimal control forced through a robust model predictive controller,enabling coordinated operation of train groups under the new virtual formation mode.Simulation results demonstrate that,compared to traditional model predictive control,the proposed method can exhibit superior performance in train speed tracking error,acceleration fluctuation range,and control force output when subjected to identical external disturbances.Speed tracking accuracy can improve by up to 47%,maximum operational acceleration decreased by up to 16.8%.The control force variation is reduced,confirming the method's robust disturbance rejection capability.Furthermore,when encountering sudden disturbances,the proposed method can reduce the time required for virtual formation trains to complete marshalling.It also exhibits superior stability during emergency braking scenarios.These findings can provide valuable insights for enhancing the operational safety of high-speed trains and improving the transport efficiency of high-speed rail networks.