Formation control for uncertain multiple Euler-Lagrange systems with dynamic surface control and interval type-2 neuro-fuzzy networks

  • Wei Shou Chan
  • , Chun Lin Chen
  • , Yeong Hwa Chang
  • , Chia Wen Chang
  • , Hung Wei Lin

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

1 Scopus citations

Abstract

This paper presents a distributed adaptive formation control method for a class of uncertain multiple Euler-Lagrange systems. The proposed approach is based on the graph theory and an adaptive dynamic surface control, where the system uncertainties are approximately modelled by interval type-2 neuro-fuzzy networks. In this study, the robust stability of the closed-loop system is guaranteed by the Lyapunov theorem, and all agents reach a desired formation following a designated trajectory. In addition to simulation example, the proposed method is applied to each agents of Euler-Lagrange dynamics for performance evaluations. Simulation results indicate that the proposed control scheme has superior responses compared to distributed dynamic surface formation control.

Original languageEnglish
Title of host publication11th IEEE International Conference on Control and Automation, IEEE ICCA 2014
PublisherIEEE Computer Society
Pages231-236
Number of pages6
ISBN (Print)9781479928378
DOIs
StatePublished - 2014
Event11th IEEE International Conference on Control and Automation, IEEE ICCA 2014 - Taichung, Taiwan
Duration: 18 06 201420 06 2014

Publication series

NameIEEE International Conference on Control and Automation, ICCA
ISSN (Print)1948-3449
ISSN (Electronic)1948-3457

Conference

Conference11th IEEE International Conference on Control and Automation, IEEE ICCA 2014
Country/TerritoryTaiwan
CityTaichung
Period18/06/1420/06/14

Keywords

  • Multiple Euler-Lagrange system
  • dynamic surface control
  • formation control
  • interval type-2 neuro-fuzzy network

Fingerprint

Dive into the research topics of 'Formation control for uncertain multiple Euler-Lagrange systems with dynamic surface control and interval type-2 neuro-fuzzy networks'. Together they form a unique fingerprint.

Cite this