Research On Automatic Driving Control Of High-Speed Maglev Train Considering Multi-Index Optimization

Automatic driving control is an important technology in high-speed maglev transportation system.Although the research on the automatic driving control of wheel-rail train has made abundant achievements,considering that the high-speed maglev train is quite different from the wheel-rail train in terms of traction,speed measurement and positioning,stopping brake,the relevant research results cannot be directly applied to the high-speed maglev train.Therefore,it is of great theoretical significance and application value to study the automatic driving control of high-speed maglev train.Firstly,from the perspective of multi-objective optimization,considering the energy saving,punctuality and comfort of passengers of the high-speed maglev train,the improved multi-objective particle swarm optimization algorithm is used to optimize the train target speed curve.Secondly,according to the operation characteristics of the high-speed maglev train,an automatic driving controller based on iterative learning control is designed and optimized.Finally,a hardware-in-loop simulation platform is designed and built aiming at the functionality test of high-speed maglev transportation operation control system.Based on this platform,the kinematics model of the train and the automatic driving control algorithm are simulated and verified.The main research contents and innovations of this thesis are summarized as follows :1.By analyzing the longitudinal force of high-speed maglev train,the kinematic model of the train is established and discretized.The running characteristics of the train under different working conditions are simulated to verify the rationality of the model.2.Aiming at the target speed curve optimization problem of high-speed maglev train,an optimization scheme considering multiple operation indexes is proposed,and an improved multi-objective particle swarm optimization algorithm is proposed.This algorithm introduces external files to store the Pareto optimal solution found by particles in the search process,which plays a better guiding role for the population,and can effectively avoid the problem that the target solution falls into local optimum.3.According to the operation characteristics of high-speed maglev train,an automatic driving controller based on PD iterative control algorithm is designed.For the problem of non-repetitive disturbance in the system,the feedback term is introduced into the controller,thereby weakening the influence of non-repetitive disturbance on the automatic train driving control.Aiming at the overspeed protection problem of high-speed maglev train,the overspeed protection penalty is further introduced into the controller,so as to effectively prevent the overspeed phenomenon of the train and ensure the traffic safety.4.A hardware-in-the-loop simulation platform is designed and built for the functional test of the operation control system.Based on the simulation platform,the vehicle motion simulation and traction control simulation software are programmed.The train kinematics model and automatic driving control algorithm are integrated into the simulation system,respectively.The rationality of the train model and the feasibility of the control algorithm are verified.This thesis contains 54 figures,8 tables and 63 references.