Neural adaptive coordination control of multiple trains under bidirectional communication topology

Shigen Gao, Hairong Dong, Bin Ning, Clive Roberts, Lei Chen

Research output: Contribution to journalArticlepeer-review

10 Citations (Scopus)

Abstract

This paper investigates the problem of coordination control for a group of trains by neural adaptive approach. The communication structure among trains is a bidirectional one, i.e., necessary information of neighboring trains is used in the control design for a train. Two control schemes are developed, with the first one requiring the information of position, speed, and acceleration of neighboring trains, while the second requiring the information of position of neighboring trains only by virtue of high-order sliding mode observer technique. Based on the universal approximation capacity of radial basis function neural networks, there are no requirements of the precise parameters describing operational resistance and other kinds of extra resistances in the controller design, which are reconstructed by radial basis function neural networks online. The stability of single train and multiple trains are guaranteed by Lyapunov stability theorem. Numerical simulations are presented to demonstrate the effectiveness and performance of the proposed controllers.

Original languageEnglish
Pages (from-to)2497-2507
Number of pages11
JournalNeural Computing and Applications
Volume27
Issue number8
Early online date1 Sep 2015
DOIs
Publication statusPublished - Nov 2016

Keywords

  • Bidirectional communication
  • Multiple train coordination
  • Neural adaptive control

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

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