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Neural Computing and Applications
Erschienen in: Neural Computing and Applications 1/2015

01.01.2015 | Original Article

Adaptive fault-tolerant automatic train operation using RBF neural networks

verfasst von: Shigen Gao, Hairong Dong, Bin Ning, Yao Chen, Xubin Sun

Erschienen in: Neural Computing and Applications | Ausgabe 1/2015

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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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Metadaten
Titel
Adaptive fault-tolerant automatic train operation using RBF neural networks
verfasst von
Shigen Gao
Hairong Dong
Bin Ning
Yao Chen
Xubin Sun
Publikationsdatum
01.01.2015
Verlag
Springer London
Erschienen in
Neural Computing and Applications / Ausgabe 1/2015
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-014-1705-y

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