Research On Control Strategy Of The Unmanned Urban Rail Vehicle Precision Stop

In order to satisfy needs of being fast, efficient, punctual and low cost of urban rail transit system which develops fast nowadays, the automatic train operation(ATO) as the control core of unmanned urban rail transit system has become the main object studied by experts. ATO is applied both in ground control center and urban rail vehicle to obtain and monitor real-time data so as to ensure urban rail vehicles can run in optimal state which greatly improve the average speed, parking, operation consumption and passenger comfort. The unmanned urban rail vehicles become the future development direction of the urban rail transit system. Based on ATO system, researches on unmanned urban rail vehicles parking accuracy can not only improve the accuracy of train parking, but also has practical application values in of operation safety, operation efficiency and punctuality rate.First of all, the ATO operation conditions, urban rail vehicle traction characteristics, braking characteristics and calculation method are analyzed in this thesis. The urban rail vehicles' basic resistances and additional resistances are studied. Then, the main factors affecting the accurate parking are analyzed. And then, the vehicle dynamics model is established, which lays the foundation for the prediction of the parking error and the establishment of the exact parking algorithmSecondly, the time series auto regression model is introduced. White noise test and stability test are carried out on the sample data of the prediction object. Then, the type of time series model is elected. After that, the model order is determined by calculating the auto--correlation coefficient and the partial autocorrelation coefficient. Then the model parameters are given through maximum likelihood estimation method, and the validity of the model is tested. The simulation results show that the model is feasible and effective.Then, the application of the algorithm and the control strategy of the unmanned urban rail vehicle is introduced. Modeling assumptions and preconditions are prepared. Then, several kinds of braking strategies and objective functions are established, and the penalty function is established according to the speed limit of the city rail vehicle system. Finally, the control strategy of urban rail vehicles is achieved by combining different braking algorithms.Finally, unmanned urban rail vehicle stopping operation is simulated with fixed penalty factors and variable penalty factors in three conditions including the same braking distance of different initial velocities, the same initial velocity of different braking distances and the different braking distances of different initial velocities. The optimal iterative penalty factors and the optimal states in different forms are shown in simulation results. The simulation results also tell the optimal operating states with vehicle braking and optimal iteration parameters of the precision stop algorithm which prove the control strategy is reasonable and effective.