A quasi-ARX model incorporating neural network for control of nonlinear systems

Jinglu Hu, Kotaro Hirasawa, Kousuke Kumamaru

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

Neural networks have been known as flexible nonlinear black-box models and have attracted much interest in control community. This paper introduces a new neural-network based prediction model for control of nonlinear systems. Distinctive features of the new model to the conventional neural-network based ones are that it has not only meaningful interpretation on part of its parameters but also is linear for the input variables. The former feature makes the parameter estimation easier and the latter allows deriving a nonlinear controller directly from the identified prediction model. The modeling and the parameter estimation are described in detail. The usefulness of the new model is demonstrated by applying it to control of two simulated nonlinear black-box systems.

Original languageEnglish
Title of host publicationIFAC Proceedings Volumes (IFAC-PapersOnline)
EditorsGabriel Ferrate, Eduardo F. Camacho, Luis Basanez, Juan. A. de la Puente
PublisherIFAC Secretariat
Pages199-204
Number of pages6
Edition1
ISBN (Print)9783902661746
DOIs
Publication statusPublished - 2002
Externally publishedYes
Event15th World Congress of the International Federation of Automatic Control, 2002 - Barcelona, Spain
Duration: 2002 Jul 212002 Jul 26

Publication series

NameIFAC Proceedings Volumes (IFAC-PapersOnline)
Number1
Volume15
ISSN (Print)1474-6670

Other

Other15th World Congress of the International Federation of Automatic Control, 2002
CountrySpain
CityBarcelona
Period02/7/2102/7/26

Keywords

  • Neural network
  • Nonlinear control
  • Nonlinear model
  • Parameter estimation
  • System modeling

ASJC Scopus subject areas

  • Control and Systems Engineering

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