Study On Optimization Of ATO Target Speed Curve And It's Tracking Control

One important subsystem of Train Operation Control system is Automatic train operation (ATO) subsystem. ATO is under the automatic train protection (ATP) subsystem's monitoring to ensure the safety, on-time and high-efficiency of railway transportation. At present, along with the rapid development of railway construction, the continuous improvement of the operation speed and the growing pressure on traffic density, the artificially driving could not meet the requirements of the train operation control. Therefore, with the current advanced computer control technology, the design of the efficiency and intelligent ATO system is very important. And this system can not only improve the efficiency, but also reduce the labor intensity. In this thesis, according to the feature of high-speed railway protection, it studies deeply in the ATO algorithm of the high-speed railway based on the particle swarm optimization algorithm and the fuzzy self-adaptive control theory.Firstly, based on the analysis of the structure and function of the ATO system, this thesis summarizes the train operation strategies and the optimizing operation principle, and establishes the model for each performance parameter of the ATO system. Secondly, according to the conditions of the train's operation process, it builds the train's ATO targets speed curve based on the particle swarm optimization, and the system can achieve the goals of punctuality precision parking, comfortable and energy-saving. And then, it studies the feasibility of combing with the fuzzy control and self-adaptive control. Next, with the advantages of them, the application of the method of the fuzzy self-adaptive control to design the ATO speed follow controller, and analysis the performance parameter of it comparing with the PID controller. At last, it takes the CRH2 series trains as an example, and makes the simulation software to verify this algorithm, which is based on the data of the typical route.This thesis tries to apply the particle swarm optimization and fuzzy self-adaptive control technology to the controlling of ATO system. The simulation results show that this algorithm can meet each performance demands of the controlling of ATO system on the train operation. The study expands the minds of the research and development of ATO system.