Research On Optimization Of Automatic Train Operation System Based On Intelligent Control Algorithm

As China's urbanization process, the continuing development of city size and increase of the urban population, existing urban traffic net is unable to meet the demands of city traffic. According to the international large-scale urban transportation developing experience, UMT plays the pivotal role for easing urban traffic pressure. Under this background, it is important to develop the the highly effective intelligence ATO system because ATO can improve the operationg efficiency, speed up the train pace, and ensure the safety.This article discussed the ATO system's basic structure, function and the automatic operation principle of the train to point out that using the PID controller based on the tradition control theory has cannot adapt the train movement parameters' misalignment and time-variable. It is very difficult to reflect the actual situation really, thus causes the speed control smoothness to receive the destruction, the hauling and the brake cut is frequent, and affects the system comfortable request. Simultaneously has also affected train's energy conservation performance and the parking precision. But we can solve the above questions effectively by using the advanced intelligent control method to carries the optimization on the traditional controller and the tracing curve.The Fuzzy Control, the Neural Network and the Genetic Algorithm are the three kind of intelligent algorithms which develop quickly in recent years. They can imitate driver's operation behavior in varying degrees, therefore can be used to optimize and improve the traditional ATO control system. This article introduced the Fuzzy Control, the Neural Network and the Genetic Algorithm's using in optimizing the PID control system and the target curve in turn, and has carried on the modelling simulation with Matlab and the Simlink toolboxs. Main contents are as follows:(1) Through the research of the ATO system's structure and the function, summarize the ATO system's driving strategy and the optimized operation principle, analyze performance index which the ATO system must achieves, and on this basis we carry on the modelling to the train movement.(2) Design the ATO algorithm. First we design the PID controller according to the traditional control algorithm so as to track the train goal curve driving, on this basis introduce the intelligent control algorithms: the Fuzzy Control, the Neural Network and the Genetic Algorithm, which are used to the optimize and improve the traditional PID controller and the target curve.(3) Realizes the three kind of intelligent algorithms' application in the ATO system's simulation modelling in turn. On the basis of the above three kind of intelligent algorithms, design the more outstanding PID controller and the more reasonable target curve according to five performance requirements which the ATO system optimization operation must achieve, and carriy on the appraisal analysis to its performance.(4) Test the optimized ATO system's whole control effection by simulation and confirmation. In the situation of selection actual line parameters and train parameters, analyze the ATO systems control train movement curve which has been optimized based on the above three kind of intelligent algorithm's simulation, after that draw the conclusion of the intelligent algorithm's rational.