Research on Improved Train Automatic Control Strategy Based on Particle Swarm Optimization

Rail transit plays an important role in alleviating urban traffic pressure and improving urban traffic capacity. The traditional train automatic driving (ATO) system control strategy accurately controls the switching of working conditions by improving the tracking accuracy of the target speed. This method consumes a large amount of energy and cannot be globally optimized. In order to better solve the defects of traditional control strategies, this paper proposes a method based on particle swarm optimization to optimize multi-objective control strategy. Under the premise of ensuring the safety of train operation and meeting the requirements of train on-time, energy saving, comfort and accurate parking, the multi-objective optimization model of train automatic control is established, and the best conversion point of train conditions is found to control train operation. The group algorithm optimizes the train automatic control strategy, and finally verifies the feasibility and effectiveness of the design scheme through experimental simulation.