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Adaptive fault-tolerant automatic train operation using RBF neural networks

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Abstract

In order to accommodate actuator failures which are unknown in amplitude and time, adaptive fault-tolerant control schemes are proposed for automatic train operation system. Firstly a basic design scheme on the basis of direct adaptive control is considered. It is demonstrated that, when actuator failures occur, asymptotical speed and position tracking are guaranteed. Then a new user-friendly control scheme is proposed which can eliminate the undesirable chattering phenomenon, which is the defect of the previous method. Simulation results verify the effectiveness of established theoretical results that satisfactory speed tracking and position tracking can be guaranteed in the presence of uncertain actuator failures in automatic train operation systems.

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Acknowledgments

This work is supported jointly by the Fundamental Research Funds for Central Universities (No. 2013JBZ007), National Natural Science Foundation of China (No. 61322307, No. 61304157, No. 61304196) and Beijing Jiaotong University Research Program (No. RCS2012ZZ003). Part of this work was completed while Mr. Shigen Gao was Visiting Research Student in University of Birmingham, and the financial support of China Scholarship Council is gratefully appreciated.

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Authors and Affiliations

  1. State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing, 100044, China

    Shigen Gao, Hairong Dong & Bin Ning

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

    Yao Chen & Xubin Sun

Authors
  1. Shigen Gao
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  2. Hairong Dong
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  3. Bin Ning
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  4. Yao Chen
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  5. Xubin Sun
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Correspondence to Shigen Gao.

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Gao, S., Dong, H., Ning, B. et al. Adaptive fault-tolerant automatic train operation using RBF neural networks. Neural Comput & Applic 26, 141–149 (2015). https://doi.org/10.1007/s00521-014-1705-y

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