Urban rail train intelligent control method based on driving experience and online study

Abstract

The invention discloses an urban-railway-system train intelligent control method based on driving experience and online study. The method obtains output of a controller through simulation of a driving strategy of an experienced driver, use of real-time data of train operation and reasoning and induction, and uses real-time information and speed-limit information of the trains to reasonably distribute the remaining time of train operation so that a time precision estimation value and a speed estimation value are obtained, and based on an MTDA algorithm of the real-time information, the method uses a train operation limit speed and current position, speed and time to deduct an expectation operation time and an expectation operation speed of train operation. Compared with a simulation result of automatic train operation (ATO) of a Yizhuang line of the Beijing city, the intelligent control method is capable of saving energy and improving on-time performance.

Description

technical field [0001] The invention relates to the field of rail traffic control, in particular to an intelligent control method for urban rail transit (urban rail) trains based on driving experience and online learning. Background technique [0002] The automatic train control system ATC (Automatic Train Control) adopts advanced automatic train control technology, which can greatly improve the efficiency and safety of trains, and is essential for the high-efficiency and high-density requirements of urban rail transit. ATC system generally includes ATS (Automatic Train Supervision) system, ATP (Automatic Train Protection) system and ATO (Automatic Train Operation). Among them, ATO, as an important subsystem of the automatic train control system, uses on-board solidified information and ground information to realize the control of train traction and braking, so that the train is always in the best running state, improving passenger comfort and improving train punctuality ra...

AI Technical Summary

Problems solved by technology

The disadvantage of this method is that there are too many times of acceleration and deceleration switching when controlling the speed. This situation is not conducive to smooth operation, but also destroys the comfort of the ride. At the same time, it also increases energy consumption and reduces parking accuracy. The operation planning time is adjusted flexibly; in addition, the frequent switching of the controller also affects the service life of the controller

[0004] In addition, the ATO control algorithm also includes methods such as adaptive robust control, iterative learning control, and fuzzy control, but these methods require offline target speed curves, and it is difficult to take into account issues such as passenger comfort during train operation; and these control It is difficult for the algorithm to fully consider the complex train model, and during the operation of the train, it will be disturbed by unknown factors, such as uncertain friction, the force between the carriages and other factors, and the nonlinearity of the output of the train controller characteristics and saturation characteristics, it is difficult to achieve accurate tracking, and it is difficult to achieve online-intelligent driving strategies

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