Open Access

ARTICLE

Predictive Control Algorithm for Urban Rail Train Brake Control System Based on T-S Fuzzy Model

by Xiaokan Wang, Qiong Wang, Shuang Liang

1 School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing, 100044, China.
2 Henan Mechanical and Electrical Vocational College, Xinzheng, 451191, China.
3 University of Florence,Via Sandro Pertini, Firenze, 50041, Italy.

* Corresponding Author: Xiaokan Wang. Email: .

Computers, Materials & Continua 2020, 64(3), 1859-1867. https://doi.org/10.32604/cmc.2020.011032

Received 15 April 2020; Accepted 23 April 2020; Issue published 30 June 2020

Abstract

Urban rail transit has the advantages of large traffic capacity, high punctuality and zero congestion, and it plays an increasingly important role in modern urban life. Braking system is an important system of urban rail train, which directly affects the performance and safety of train operation and impacts passenger comfort. The braking performance of urban rail trains is directly related to the improvement of train speed and transportation capacity. Also, urban rail transit has the characteristics of high speed, short station distance, frequent starting, and frequent braking. This makes the braking control system constitute a time-varying, time-delaying and nonlinear control system, especially the braking force changes directly disturb the parking accuracy and comfort. To solve these issues, a predictive control algorithm based on T-S fuzzy model was proposed and applied to the train braking control system. Compared with the traditional PID control algorithm and self-adaptive fuzzy PID control algorithm, the braking capacity of urban rail train was improved by 8%. The algorithm can achieve fast and accurate synchronous braking, thereby overcoming the dynamic influence of the uncertainty, hysteresis and time-varying factors of the controlled object. Finally, the desired control objectives can be achieved, the system will have superior robustness, stability and comfort.

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Citations