## 7 Conclusions  
Taking the automatic train driving system of urban rail as the research object, the precise control algorithm of urban rail trains is studied in this paper. Combined with the train dynamics model, the mathematical description of the train control process is given. Aiming at the precise control technology in automatic train operation, an adaptive terminal sliding mode controller is proposed. The terminal sliding mode control is used to design the control algorithm, and the parameter adaptive mechanism is introduced to enhance the control system adaptability. Based on the ATO algorithm of adaptive terminal sliding mode control, the RBFNN disturbance approximator is designed, and the disturbance value of the RBFNN disturbance approximator is introduced into the control algorithm to enhance the robustness and anti-disturbance ability of the system. At the same time, the problem of input saturation caused by train traction or braking force cannot be infinite, the transmission delay of speed sensor and the partial failure of traction or braking actuator in ATO system are considered, and the fault-tolerant mechanism is introduced. The simulation results show that when the model has parameter uncertainty and external disturbance, the designed control algorithm can synchronously compensate and deal with the nonlinear effects such as control input saturation, time delay, and actuator failure to ensure the accurate control of train operation.  
Combined with the research content of this paper, the future work that should be carried out in-depth research is as follows:  
* Research on the optimization strategy and control method of automatic train operation based on deep learning and knowledge automation. In this paper, from the perspective of mechanism modeling, the train dynamics model is obtained by analyzing the force of the train. However, the massive data generated during the actual operation of the train is not fully utilized and excavated. Mining rules from historical data and accurate prediction will help to grasp the train operation status in detail and provide more decision support for the dispatching command;
* Research on the optimization strategy and control method of multi-train intelligent operation for multi-train cooperative operation. Under the conditions of high-density and long-period operation of urban rail trains, multi-train collaborative control is a solution to achieve global optimization and ensure system performance. The development of advanced train-to-train communication, virtual marshalling, and other technologies and their application in multi-train operation control systems are the development trend and inevitable trend in recent years.  
Conceptualization, J.Y. and Y.Z.; Data curation, J.Y.; Formal analysis, J.Y.; Investigation, J.Y., Y.Z. and Y.J.; Methodology, J.Y.; Project administration, Y.Z.; Resources, J.Y., Y.Z. and Y.J.; Software, J.Y.; Supervision, Y.Z.; Validation, Y.J.; Visualization, Y.Z. and Y.J.; Writing--original draft, J.Y.; Writing--review & editing, J.Y. All authors have read and agreed to the published version of the manuscript.  
This research was funded by NSFC, Grant No. 61963023.  
Data Availability Statement: Data sharing not applicable.