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Application of fuzzy predictive control technology in automatic train operation

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Abstract

In order to better control the train operation system, a typical complex, multi-objective and nonlinear system is discussed. In this study, fuzzy predictive control technology is used to provide high quality control conditions for train operation, which provides great potential for the control of complex system. It is difficult to find the accurate mathematical model and the optimal solution. First, the basic structure and function of train automatic control system are introduced, especially the coordination between automatic train operation (ATO) subsystem and other subsystems. Then, the basic principles of fuzzy logic and predictive control are introduced, and various forms of fuzzy logic and predictive control are analyzed. The application and simulation of fuzzy predictive control in ATO system are deeply studied. Fuzzy predictive control for speed following system of ATO is designed. The fuzzy predictive control technology is compared with the conventional control technology. The simulation results show that the performance of train safety, comfort, parking accuracy and other performance indicators have been improved significantly by using fuzzy predictive controller. In conclusion, the fuzzy predictive controller can realize the control of ATO system better.

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Acknowledgements

The authors acknowledge the National Natural Science Foundation of China (Nos. U1534208 and U1734211) and the Fundamental Research Funds for the Central Universities (No. 2017JBZ109).

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Authors and Affiliations

  1. National Engineering Research Center of Rail Transportation Operation and Control System, Beijing Jiaotong University, Beijing, 100044, China

    Yuan Cao & Lianchuan Ma

  2. School of Electric and Information Engineering, Beijing Jiaotong University, Beijing, 100044, China

    Yuan Cao, Lianchuan Ma & Yuzhuo Zhang

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  1. Yuan Cao
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  2. Lianchuan Ma
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  3. Yuzhuo Zhang
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Correspondence to Lianchuan Ma.

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Cao, Y., Ma, L. & Zhang, Y. Application of fuzzy predictive control technology in automatic train operation. Cluster Comput 22 (Suppl 6), 14135–14144 (2019). https://doi.org/10.1007/s10586-018-2258-0

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  • DOI: https://doi.org/10.1007/s10586-018-2258-0

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