Method and device for train control, electronic equipment and storage medium

Abstract

The invention relates to the technical field of train automatic driving, and provides a method and device for train control, electronic equipment and a storage medium. The method comprises the steps that the actual speed of a current train and the actual distance between the current train and a front train are obtained; comparing the actual vehicle speed with a preset expected vehicle speed to obtain an actual speed deviation, and comparing the actual distance with a preset expected distance to obtain an actual distance deviation; performing table look-up operation according to the actual speed deviation and the actual distance deviation to obtain an actual optimal speed weight interval and an actual optimal distance weight interval; respectively acquiring a speed weight parameter and a distance weight parameter according to the actual optimal speed weight interval and the actual optimal distance weight interval; acquiring the acceleration corresponding to the current train according to the speed weight parameter and the distance weight parameter; acquiring a level signal according to the acceleration; and the current train is controlled according to the level signal, so that stable train control can be realized.

Description

technical field [0001] The present application relates to the technical field of train automatic driving, and in particular, relates to a method and device for train control, electronic equipment, and a storage medium. Background technique [0002] With the vigorous development of urban rail transit in my country, automatic driving of trains has become an inevitable trend in the future. The mainstream control algorithm mostly adopts the model predictive control algorithm, but using this algorithm alone has certain disadvantages. Compared with traditional control algorithms such as PID, the model predictive control algorithm has more control parameters and is not easy to determine. If the parameter settings are unreasonable, it is easy to cause poor vehicle control effect and fail to meet the control requirements. Most of the parameters of the model predictive control algorithm adopted in the prior art are fixed, that is, after the parameters are set, they are common through...

AI Technical Summary

Problems solved by technology

However, in the actual car control process, there will be different vehicle running states, so using a single set parameter will easily lead to poor car control effect, and it is difficult to achieve the purpose of stable car control

However, the traditional model predictive control algorithm is difficult to achieve stable vehicle control under different vehicle operating conditions.

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