Research On Accurate Stopping Of High-speed Trains Based On Model Predictive Control

Automatic train operation(ATO)is an emerging technology to improve the efficiency,ride comfort,and energy efficiency of high-speed trains.Accurate stopping is a key function of the ATO system,and it aims to make a train stop safely and accurately at the designated position in a station while ensuring the ride comfort.In recent years,the platform screen doors have been used in high-speed railways,a higher stopping accuracy is required to ensure that passengers are not disturbed when getting on and off on these platforms.In this thesis,the automatic train stop control model of high-speed trains is first be studied,and then further research is conducted on model predictive control(MPC)based stopping method.In order to improve the ride comfort and the performance at the complex environment,the improved MPC based stopping method is proposed,which can realize high-precision speed tracking and avoid frequent switching of the control output.The analytical performance of these methods can be ensured.Specifically,this thesis aims to make the following contributions on the study of the high-speed train stop control problem.1.A model and an identification method is developed for the high-speed train stop control problem.The braking process of high-speed trains is formulated as a linear time delay process based on the real-life data.A Picard iteration based parameter estimation method is extended to time delay systems.The convergence of the method is ensured by Banach fixed point theorem,and the method can achieve effective estimation while the persistent excitation condition holds.2.An MPC based train stop control method is presented according to characteristics of train stopping processes.The stopping process is considered as a speed tracking control problem with several hard constraints.MPC is an effective method for multivariable constrained control problems.Therefore,MPC is introduced to the automatic train stop control problem under the assumption of a known external resistance.The stability and sub-optimality of the approach can be guaranteed.3.Considering the uncertain external disturbance and the ride comfort,an improved MPC based automatic train stop control method is developed.A robust model predictive control(RMPC)method is adopted to improve the robustness for external disturbances.Then a self-triggered model predictive control(RSMPC)approach is introduced to reduce the frequent switches of the control output.The robust stability and sub-optimality of the approach can be guaranteed.The simulation results based on the real-life operation data verify the effectiveness of the proposed method,and the required stopping accuracy(±50cm)can be ensured.