Adaptive backstepping tracking control using self-organizing fuzzy neural network

Chih Min Lin, Chun Fei Hsu, I. Fang Chung

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

Abstract

This paper proposes an adaptive backstepping tracking control (ABTC) via self-organizing fuzzy neural network (SOFNN) approach. The proposed ABTC system is comprised of a backstepping tracking controller and an L1 controller. The backstepping tracking controller containing a SOFNN identifier is the principal controller, and the L, controller is designed to achieve a tracking performance with desired attenuation level. The SOFNN identifier is used to online estimate the system dynamics with structure and parameter learning. Finally, the proposed ABTC is applied to control a chaotic dynamic system. The simulation results verify that the proposed ABTC system can achieve favorable tracking performance by incorporating of neural network approach and adaptive backstepping control technique.

Original languageEnglish
Title of host publicationIMECS 2006 - International MultiConference of Engineers and Computer Scientists 2006
Pages54-59
Number of pages6
StatePublished - Jun 2006
EventInternational MultiConference of Engineers and Computer Scientists 2006, IMECS 2006 - Kowloon, Hong Kong
Duration: 20 Jun 200622 Jun 2006

Publication series

NameLecture Notes in Engineering and Computer Science
ISSN (Print)2078-0958

Conference

ConferenceInternational MultiConference of Engineers and Computer Scientists 2006, IMECS 2006
Country/TerritoryHong Kong
CityKowloon
Period20/06/0622/06/06

Keywords

  • Adaptive control
  • Backstepping control
  • Robust control
  • Rule elimination
  • Rule generation

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