Neural adaptive fault-tolerant control for high-speed trains with input saturation and unknown disturbance

The problem of the position and velocity tracking control for high-speed trains (HSTs) subject to unknown basic resistance, extra resistance and actuator faults is investigated. Neural adaptive control strategies based on a novel sliding mode surface technique are presented for three different cases...

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Bibliographic Details
Published inNeurocomputing (Amsterdam) Vol. 260; pp. 32 - 42
Main Authors Lin, Xue, Dong, Hairong, Yao, Xiuming, Bai, Weiqi
Format Journal Article
LanguageEnglish
Published Elsevier B.V 18.10.2017
Subjects
Fault-tolerant control
High-speed trains (HSTs)
Input saturation
Neural networks
Sliding mode surface
High-speed trains (HSTs)
Input saturation
Fault-tolerant control
Neural networks
Sliding mode surface
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ISSN0925-2312
1872-8286
DOI10.1016/j.neucom.2017.02.083

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Summary:The problem of the position and velocity tracking control for high-speed trains (HSTs) subject to unknown basic resistance, extra resistance and actuator faults is investigated. Neural adaptive control strategies based on a novel sliding mode surface technique are presented for three different cases (actuator faults and input saturation are neither considered; only the former is considered, and both are considered) to tackle the problem. For each case, the radial basis function (RBF) neural networks is introduced to approximate the unknown extra resistance consisting of ramp resistance, tunnel resistance, curve resistance, and so on; unknown coefficients of sliding mode surface and dynamics formulation are obtained online via adaptive laws. Simulation results demonstrate the effectiveness of the presented control methodologies.
ISSN:0925-2312
1872-8286
DOI:10.1016/j.neucom.2017.02.083