Research On Speed Curve Optimization And Tracking Control Of High-speed Train Based On Particle Swarm Optimization Algorithm

China’s railway has experienced more than a decade of rapid development.In order to promote high-level opening up and better improve the travel quality of the people,the requirements for the development of China’s railway industry are also constantly improving.However,when the safety and punctuality of the train are satisfied,the energy consumption in the train operation is high.In order to reduce the train energy consumption and improve the train operating efficiency and performance,it is of great significance to optimize and track the train operating speed curve.This paper mainly studies the optimization and control of high-speed train operation from the optimization of train speed curve and tracking control speed curve.Specific research contents are as follows:(1)Based on the train kinematics model,the train resistance model is improved according to the actual train running condition,which can better reflect the train running condition.In addition,the train operation strategy and operating condition transition rules are analyzed in detail,which provides a model basis for optimizing and tracking the train operating speed curve.(2)For standard Particle Swarm Algorithm premature convergence and fall into the local optimal problem,this paper puts forward Adaptive Particle Swarm Optimization(APSO)Algorithm based on the diversity of the feedback,analyzes the diversity evaluation mechanism of the algorithm,adaptive inertia weight control and elite learning process,and improved the algorithm of the inertia weight value scope.Compared with the standard particle swarm optimization algorithm,the searching ability and convergence speed of the algorithm are enhanced,and the premature convergence problem is avoided.Then,the adaptive particle swarm optimization algorithm was applied to solve the optimization target model of the train.MATLAB simulation software is used to simulate the process of optimizing the train running speed curve of the algorithm,and the optimal train running speed curve is obtained.This process fully verifies that the adaptive particle swarm optimization algorithm can effectively optimize the train speed curve.(3)Aiming at the problem of tracking the speed curve of high-speed trains,according to the characteristics of the uncertainty of the actual running conditions of trains,this paper designed a double-closed-loop sliding mode controller based on the sliding mode control theory,and proved the stability of the controller by using the Lyapunov theory.Finally,based on the actual line data of qinhuangdao high-speed railway and shenyang high-speed railway,MATLAB software is used to simulate the process of the controller tracking the train running speed curve.The simulation results show that the controller can track and control the train speed curve,and the validity of the control law is verified.