Research On ATO Multi-objective Optimization Of High-speed Train Tracking Operation Based On Multi-particle Model

Since theirs inception,high-speed railways have quickly become the core framework of China’s land-based integrated transportation system with theirs economic advantages such as safety,speedy,and environmental friendly,and theirs play a vital role in promoting social and economic development,safeguarding and improving people’s livelihood,and improving people’s sense of happiness and gain.However,many opportunities and challenges,such as the increase of operating speed,the expansion of the scale of operations,and the exponential growth of the scale and number of cities,have put forward more stringent requirements on high-speed railway technology.On the road of safer,more efficient,more energy-saving and more comfortable development of high-speed trains,automatic train operation has become a new development trend in the future.The automatic train operation system is not only the essential equipment to improve the operation efficiency of the section and increase the density of the interval traffic,but also can computer control capabilities to avoid errors caused by uncertain factors and greatly reduce the driver’s labor intensity and ensure the efficient and safe operation of the train.This thesis mainly completed the following research content:Firstly,the working principle and main functions of high-speed automatic train operation system are discussed,and a multi-objective optimization scheme of automatic train operation with two steps about curve optimization and curve tracking is put forward.By analyzing the evaluation indexes of high-speed automatic train operation,a multi-objective optimization index model is established and a hybrid optimal control strategy is proposed based on the traditional automatic driving control strategy.Secondly,aiming at the curve optimization process,through dynamic analysis and kinematics analysis,the single-particle calculation model,the multi-particle calculation model and kinematics model of high-speed trains are established,and a method for multi-objective curve optimization based on the new seeker optimization algorithm is proposed to generate the ideal speed-distance curve.Finally,based on the analysis of traditional proportional integral differential controller,combined with the new seeker optimization algorithm,a new automatic train operation speed controller is designed,and a curve tracking simulation module is built with the SIMULINK platform to complete the function of the autopilot control layer.In the simulation verification stage,the concrete high-speed railways and the China Railway High-speed EMU are selected,based on the MATLAB platform,the seeker optimization algorithm,hybrid optimal control strategy,parameter setting method of intelligent algorithm,curve optimization method of multi-objective and the speed controller tracking proposed in this thesis are simulated and verified,and the performance of multiple indicators is analyzed.The feasibility of the multi-particle calculation model and seeker optimization algorithm used in this thesis in the process of automatic train operation curve optimization of high-speed trains is obtained,which proves the applicability of the speed controller based on seeker optimization algorithm in the process of curve tracking.It can be seen that the research work of this thesis has certain theoretical significance and application value.