Skip to main navigation Skip to search Skip to main content

Cooperative Adaptive Control of Multiple High-speed Trains with Constrained Input via Learning Signal Trammeling

  • Yu'nan Yang
  • , Shigen Gao*
  • , Hairong Dong
  • , Wei Zheng
  • , Fei Hao
  • , Qing Li
  • *Corresponding author for this work
  • Beijing Jiaotong University
  • Beihang University
  • Ltd.

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

This paper develops a learning signal trammeling based cooperative adaptive control for multiple high-speed trains in the presence of constrained input nonlinearity. Using adaptive parameter identification to deal with unknown running resistances, an adaptive control is designed. Learning signal trammeling is investigated to guaranteed the closed-loop stability in the presence of constrained input. Under the proposed control, the closed-loop stability and boundedness of resulting closed-loop signals are ensured by Lyapunov stability theorem. Analytical boundaries of both position and speed tracking errors are obtained, characterizing by control parameters. Numerical simulation examples are given to verify the effectiveness of proposed control.

Original languageEnglish
Title of host publicationProceedings of the 41st Chinese Control Conference, CCC 2022
EditorsZhijun Li, Jian Sun
PublisherIEEE Computer Society
Pages5539-5543
Number of pages5
ISBN (Electronic)9789887581536
DOIs
StatePublished - 2022
Externally publishedYes
Event41st Chinese Control Conference, CCC 2022 - Hefei, China
Duration: 25 Jul 202227 Jul 2022

Publication series

NameChinese Control Conference, CCC
Volume2022-July
ISSN (Print)1934-1768
ISSN (Electronic)2161-2927

Conference

Conference41st Chinese Control Conference, CCC 2022
Country/TerritoryChina
CityHefei
Period25/07/2227/07/22

Keywords

  • High-speed train control
  • adaptive control
  • input saturation
  • parameter estimation

Fingerprint

Dive into the research topics of 'Cooperative Adaptive Control of Multiple High-speed Trains with Constrained Input via Learning Signal Trammeling'. Together they form a unique fingerprint.

Cite this