Research On Multi-objective Optimization And Speed Tracking Control Of High-speed Train Operatio

In recent years,China’s high-speed rail mileage,the number of trains,high-speed rail passenger traffic has increased year by year,high-speed trains have become the primary choice for more people to travel long distances.High-speed trains consume a lot of energy during operation,and the importance of energy saving has become more and more prominent in today’s increasingly energy-stressed world.With the improvement of people’s living standard,passengers have higher requirements for the comfort of the ride and the punctuality of train operation.Therefore,the multi-objective optimization of train operation strategy,which takes energy consumption,comfort,stopping accuracy and punctuality as the optimization targets,is of great practical significance to ensure the safe operation of trains.After the multi-objective optimization of train operation strategy,the target speed curve of train operation will be obtained,and whether the train can operate according to the target speed curve determines whether the train operation optimization strategy can be realized,and has certain reference significance for the realization of automatic train driving.In this paper,we study the multi-objective optimization and speed tracking of high-speed train operation strategy,and the main work is as follows:First,according to the research needs of the multi-objective optimization problem of highspeed train operation,the single mass point dynamics model of the train is established,and the four operating conditions of the train and the four operating performance indexes of the train are analyzed.The evaluation functions of each performance index are designed for different train operation performance indexes.The necessary conditions for the optimal train maneuvering strategy are analyzed by using the principle of great value,and the lowest energy consumption maneuvering strategy of the train is obtained.The mathematical model of the multi-objective optimization problem of train operation strategy is established by integrating the train dynamics model,operation conditions and operation performance indexes.Secondly,the theory related to the multi-objective optimization problem is analyzed,and the multi-objective particle swarm algorithm is selected as the solution algorithm for the multiobjective optimization problem of train operation strategy according to its characteristics of fast convergence,simple structure and high real time performance.For the problems of premature convergence and insufficient diversity of population particles,the adaptive inertia weight strategy,global optimal position dynamic selection strategy and mutation operator strategy are proposed to improve the algorithm.The improved multi-objective particle swarm algorithm and the multi-objective particle swarm algorithm are simulated and compared to test.The simulation results show that the improved multi-objective particle algorithm has better convergence and better distribution of the solution.Then,the improved algorithm is used to optimize the maneuvering strategy of CRH3 type locomotives on a section of the actual line of Wuhan-Guangzhou high-speed railroad,and the multi-objective particle swarm algorithm is used as the comparison algorithm.The optimization simulation tests of the operation strategy without cruise and the operation strategy with cruise are carried out respectively,and the simulation tests prove that the maneuvering strategy with cruise is better in terms of train operation performance indexes.The improved algorithm is used to obtain the target speed profile of the train under the cruising maneuvering strategy.Finally,the high-speed train speed controller is designed to track the target speed curve obtained in Chapter 3.The train simulation model and the train fuzzy sliding mode controller simulation model are established in SIMULINK according to the CRH3 moving car parameters and the fuzzy sliding mode controller designed in chapter 4.The classical PID controller is used as a comparison to test the effect of the designed controller.The simulation results show that the velocity tracking error and position tracking error of the fuzzy sliding mode controller for the target velocity profile are smaller than those of the classical PID controller,which can meet the actual operation requirements of the train.