Adaptive control of nonlinear systems using neural networks--A dead-zone approach

Fu-Chuang Chen*, Hassan K. Khalil

*Corresponding author for this work

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

40 Scopus citations

Abstract

Layered neural networks are used in a nonlinear adaptive tracking problem. The plant is an unknown feedback-linearizable discrete-time system, represented by an input-output model with relative degree higher than one. A state space model of the plant is obtained to define the zero dynamics, which are assumed to be stable. Layered neural networks are used to model the plant and generate controls. Some error between the model and the plant is allowed. A dead-zone is specified in the updating rule. A local convergence result is given.

Original languageAmerican English
Title of host publicationProceedings of the American Control Conference
Editors Anon
PublisherPubl by American Automatic Control Council
Pages667-672
Number of pages6
ISBN (Print)0879425652
DOIs
StatePublished - 1 Dec 1991
EventProceedings of the 1991 American Control Conference - Boston, MA, USA
Duration: 26 Jun 199128 Jun 1991

Publication series

NameProceedings of the American Control Conference
Volume1
ISSN (Print)0743-1619

Conference

ConferenceProceedings of the 1991 American Control Conference
CityBoston, MA, USA
Period26/06/9128/06/91

Keywords

  • Adaptive control
  • Nonlinear systems
  • Neural networks
  • Convergence
  • State-space methods
  • Neurofeedback
  • Control systems
  • Feedback control
  • Control engineering
  • Linear feedback control systems

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