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Approximation-based robust adaptive automatic train control: An approach for actuator saturation

Shigen Gao, Hairong Dong, Yao Chen, Bin Ning, Guanrong Chen, Xiaoxia Yang

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

Abstract

This paper addresses an on-line approximation-based robust adaptive control problem for the automatic train operation (ATO) system under actuator saturation caused by constraints from serving motors. A robust adaptive control law is proposed, which is proved capable of on-line estimating of the unknown system parameters and stabilizing the closed-loop system. To cope with actuator saturation, another robust adaptive control is proposed for the ATO system, by explicitly considering the actuator saturation nonlinearity other than unknown system parameters, which is also proved capable of stabilizing the closed-loop system. Simulation results are presented to verify the effectiveness of the two proposed control laws. © 2000-2011 IEEE.
Original languageEnglish
Article number6549162
Pages (from-to)1733-1742
JournalIEEE Transactions on Intelligent Transportation Systems
Volume14
Issue number4
Online published28 Jun 2013
DOIs
Publication statusPublished - Dec 2013

Research Keywords

  • Actuator saturation
  • Automatic train operation (ATO)
  • Neural network
  • Robust adaptive control

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