| With the rapid development of high-speed railway,high-speed train as a way of travel is improving people’s living standards.With the continuous acceleration of the running speed of high-speed trains,the automatic operation of high-speed trains instead of manual driving is not only the embodiment of scientific and technological progress,but also the direction of the development of high-speed trains.The main function of the Automatic Train Operation(ATO)system is to realize the autonomous operation of the high-speed train,automatically adjust the running speed and station stopping state through the preset control mode and train operation controller of the high-speed train,so as to meet the requirements of the train in terms of stationarity,punctuality,energy saving and comfort.In view of the problems existing in the research of high-speed train autopilot,an optimization method is proposed from two aspects: the generation of target speed curve and the design of train operation controller,and the high-speed train autopilot system is optimized.(1)Taking the CHR380 A as the research object,a single particle train model is established,and its running speed curve is optimized for multiple objectives.The maximum principle is used to analyze the possible most energy-saving driving strategies,including maximum traction,uniform cruising,idling and maximum braking.Aiming at the optimization problem of train speed curve,the calculation method of waiting optimization index of energy consumption,punctuality and comfort is introduced.Combined with the dynamic model of the train,the mathematical model of multi-objective optimization of high-speed train is established.(2)A Gaussian mutation multiple objective particle swarm optimization algorithm(GMOPSO)is proposed,which uses Gaussian mutation and crowding entropy to deal with elite archives,which improves the ability to search the global optimal solution and speeds up the convergence speed.In the simulation process of the algorithm,the evaluation index of multi-objective optimization problem is established.During the simulation,the operation mode of the high-speed train is optimized,compared with the conventional operation mode,the energy consumption is reduced by 15%,and the parking accuracy,on-time error and comfort are also good.The simulation results show that when solving multi-objective problems,GMOPSO algorithm has outstanding performance in energy consumption,timeliness,parking accuracy and comfort,and its convergence and distribution as well as the number of frontier solutions are better than PSO algorithm and MOPSO algorithm,which proves the superiority of the proposed GMOPSO algorithm.(3)According to the characteristics of high-speed train speed tracking control system,the PID controller is improved,and the gray prediction fuzzy PID controller is designed by combining the gray prediction theory and fuzzy controller.The controller uses the principle of gray prediction to optimize the time-lag of the fuzzy PID controller.The road section from Lanzhou West to Yuzhong was selected as the simulation route,and the control effects of PID controller,fuzzy PID controller and gray prediction fuzzy PID controller were simulated and analyzed from the aspects of tracking accuracy,punctuality,energy consumption,and parking accuracy,and the verification of all The rationality of the designed gray predictive fuzzy PID controller.(4)The gray prediction fuzzy PID controller is improved,the gray prediction model is optimized,and the solution of the multi-objective optimization model of the high-speed train and the construction of the integrated intelligent controller are completed on the MATLAB/Simulink platform.The section from Lanzhou West to Yuzhong was selected as the research line,and the designed controller was simulated and verified.The simulation results show that the GMOPSO algorithm and the improved gray predictive fuzzy PID controller are feasible and superior in speed curve optimization and speed curve tracking of high-speed trains. |
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