The Research And Simulation Of Ato Speed Controller Based On The Grey Prediction Control

With the development of China’s urbanization, the construction of efficient urban mass transit system is the trend to solve the increasingly serious urban transportation problem. For efficient and high density requirements of urban mass transit system, automatic train operation system (ATO) is an important part of the rail transit automatic train control system (ATC), this is also one of the key theory and technical problems must be solved. In recent years, the development and construction of metro is attracted universal attention by the country and big or medium-sized city, in order to reduce the investment, there is an urgent need to design and production localization. As a result, scholars and enterprises have begun to research automatic train operation, in order to improve driving strategy, the driving efficiency, passenger comfort, energy saving performance, and achieve accurate parking. Train operation is a complex, multi-objective and non-linear process, it is difficult to find the exact model and optimal solution, the conventional algorithm is difficult to meet the requirements of the automatic train operation. This paper uses a method combined the prediction algorithm and gray control, predicts and decides the handle of the next cycle, sends it to the vehicle interface to control speed, so as to achieve automatic train operation.This paper firstly introduces the ATO system and focuses on the functions of the ATO speed controller, by needs analysis, it puts forward compensatory control measures for the time lag characteristics and interference factors of the speed controller, researches performance index as punctuality, parking precision, comfort, energy saving performance, and so on, with analytical hierarchy process (AHP) for weight distribution of them on each stage of train operation. Secondly, studies the process of train operation, the system is divided into eleven statuses as stationary, starting, traction, traction load, traction uninstall, coasting, braking force loading, braking force uninstall, braking, waiting for parking, parking, and uses the combination of the prediction algorithms and gray relational to transfer train statuses, that determines the manipulation strategy of automatic train operation. Thirdly, establishes basic mathematical model of train operation, and combines with the impact factors of speed and position error, idle and slip, the delay operation, builds the automatic train operation model for traction stage, coasting stage and braking stage. Finally, the system is simulated by C language and tested on vehicle plane, verified speed control function of the ATO speed controller, and reaches the desired control effect.