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

Takayuki Furuzuki, 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)
PublisherIFAC Secretariat
Pages199-204
Number of pages6
Volume15
Edition1
Publication statusPublished - 2002
Externally publishedYes
Event15th World Congress of the International Federation of Automatic Control, 2002 - Barcelona, Spain
Duration: 2002 Jul 212002 Jul 26

Other

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

Fingerprint

Nonlinear systems
Neural networks
Parameter estimation
Controllers

Keywords

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

ASJC Scopus subject areas

  • Control and Systems Engineering

Cite this

Furuzuki, T., Hirasawa, K., & Kumamaru, K. (2002). A quasi-ARX model incorporating neural network for control of nonlinear systems. In IFAC Proceedings Volumes (IFAC-PapersOnline) (1 ed., Vol. 15, pp. 199-204). IFAC Secretariat.

A quasi-ARX model incorporating neural network for control of nonlinear systems. / Furuzuki, Takayuki; Hirasawa, Kotaro; Kumamaru, Kousuke.

IFAC Proceedings Volumes (IFAC-PapersOnline). Vol. 15 1. ed. IFAC Secretariat, 2002. p. 199-204.

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

Furuzuki, T, Hirasawa, K & Kumamaru, K 2002, A quasi-ARX model incorporating neural network for control of nonlinear systems. in IFAC Proceedings Volumes (IFAC-PapersOnline). 1 edn, vol. 15, IFAC Secretariat, pp. 199-204, 15th World Congress of the International Federation of Automatic Control, 2002, Barcelona, Spain, 02/7/21.
Furuzuki T, Hirasawa K, Kumamaru K. A quasi-ARX model incorporating neural network for control of nonlinear systems. In IFAC Proceedings Volumes (IFAC-PapersOnline). 1 ed. Vol. 15. IFAC Secretariat. 2002. p. 199-204
Furuzuki, Takayuki ; Hirasawa, Kotaro ; Kumamaru, Kousuke. / A quasi-ARX model incorporating neural network for control of nonlinear systems. IFAC Proceedings Volumes (IFAC-PapersOnline). Vol. 15 1. ed. IFAC Secretariat, 2002. pp. 199-204
@inproceedings{680daaafd2814dfa97c9c57c59841ea7,
title = "A quasi-ARX model incorporating neural network for control of nonlinear systems",
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.",
keywords = "Neural network, Nonlinear control, Nonlinear model, Parameter estimation, System modeling",
author = "Takayuki Furuzuki and Kotaro Hirasawa and Kousuke Kumamaru",
year = "2002",
language = "English",
volume = "15",
pages = "199--204",
booktitle = "IFAC Proceedings Volumes (IFAC-PapersOnline)",
publisher = "IFAC Secretariat",
edition = "1",

}

TY - GEN

T1 - A quasi-ARX model incorporating neural network for control of nonlinear systems

AU - Furuzuki, Takayuki

AU - Hirasawa, Kotaro

AU - Kumamaru, Kousuke

PY - 2002

Y1 - 2002

N2 - 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.

AB - 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.

KW - Neural network

KW - Nonlinear control

KW - Nonlinear model

KW - Parameter estimation

KW - System modeling

UR - http://www.scopus.com/inward/record.url?scp=84945540827&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84945540827&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:84945540827

VL - 15

SP - 199

EP - 204

BT - IFAC Proceedings Volumes (IFAC-PapersOnline)

PB - IFAC Secretariat

ER -