<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>The automation and intelligence of transportation vehicles is the focus and hotspot of the future research and development of the entire transportation industry, which can effectively improve a series of social problems such as traffic accidents, exhaust pollution, and traffic jams caused by the current increase in the number of transportation vehicles. With the rapid development of rail transit in our country, more and more people choose urban rail transit for travel, which is greatly convenient for travel. The comfort of Automatic Train Operation (ATO) system is an inevitable consideration when people choose it. Aiming at the comfort of ATO system, this paper designs a train operation target curve that can meet the comfort index of train. At the same time, two simulation models are established by using the SIMULINK module of MATLAB software to compare the experiments. One model is the train simulation model based on PID control, and the other model is the train simulation model based on fuzzy PID. The final simulation results show that fuzzy PID control has stronger superiority in train comfort in the process of train motion simulation, and the traditional PID control is not as good as fuzzy PID control in train comfort.</p>
</abstract>

The automation and intelligence of transportation vehicles is the focus and hotspot of the future research and development of the entire transportation industry, which can effectively improve a series of social problems such as traffic accidents, exhaust pollution, and traffic jams caused by the current increase in the number of transportation vehicles. With the rapid development of rail transit in our country, more and more people choose urban rail transit for travel, which is greatly convenient for travel. The comfort of Automatic Train Operation (ATO) system is an inevitable consideration when people choose it. Aiming at the comfort of ATO system, this paper designs a train operation target curve that can meet the comfort index of train. At the same time, two simulation models are established by using the SIMULINK module of MATLAB software to compare the experiments. One model is the train simulation model based on PID control, and the other model is the train simulation model based on fuzzy PID. The final simulation results show that fuzzy PID control has stronger superiority in train comfort in the process of train motion simulation, and the traditional PID control is not as good as fuzzy PID control in train comfort.

Simulation of Comfort Algorithm for Automatic Driving of Urban Rail Train

School of Computer Science and Engineering

International Journal of Advanced Network, Monitoring and Controls

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

{"article-title":"Simulation of Comfort Algorithm for Automatic Driving of Urban Rail Train"}

The automation and intelligence of transportation vehicles is the focus and hotspot of the future research and development of the entire transportation...

Simulation of Comfort Algorithm for Automatic Driving of Urban Rail Train

Data publikacji: 16 sie 2023

Zakres stron: 72 - 80

DOI: https://doi.org/10.2478/ijanmc-2023-0058

Słowa kluczowe
Comfort, Urban Rail Transit, Target Curve, PID Control, Fuzzy PID Control

© 2023 Kai Li et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Simulation of Comfort Algorithm for Automatic Driving of Urban Rail Train

Data publikacji: 16 sie 2023

Zakres stron: 72 - 80

DOI: https://doi.org/10.2478/ijanmc-2023-0058

Słowa kluczoweComfort, Urban Rail Transit, Target Curve, PID Control, Fuzzy PID Control

© 2023 Kai Li et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Słowa kluczowe
Comfort, Urban Rail Transit, Target Curve, PID Control, Fuzzy PID Control