Min Max control of nonlinear systems using Universal Learning Networks

Hongping Chen, Kotaro Hirasawa, Jinglu Hu, Junichi Murata

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

1 Citation (Scopus)

Abstract

A Min Max robust control method is proposed for nonlinear systems based on the use of the higher order derivatives calculation of Universal Learning Networks (ULNs). An extended criterion function containing sensitivity terms is considered for controller design and the criterion function is evaluated at several specific operating points corresponding to certain system parameters. The ULNs learning is then performed in such a way that, at each step, it minimizes the worst criterion function among several operating points. It is found that the proposed control method is less time-consuming in the ULNs learning and a obtained controller has better performance than the conventional methods.

Original languageEnglish
Title of host publicationProceedings of the International Joint Conference on Neural Networks
PublisherIEEE
Pages242-247
Number of pages6
Volume1
Publication statusPublished - 2000
EventInternational Joint Conference on Neural Networks (IJCNN'2000) - Como, Italy
Duration: Jul 24 2000Jul 27 2000

Other

OtherInternational Joint Conference on Neural Networks (IJCNN'2000)
CityComo, Italy
Period7/24/007/27/00

Fingerprint

Nonlinear systems
Controllers
Robust control
Derivatives

All Science Journal Classification (ASJC) codes

  • Software

Cite this

Chen, H., Hirasawa, K., Hu, J., & Murata, J. (2000). Min Max control of nonlinear systems using Universal Learning Networks. In Proceedings of the International Joint Conference on Neural Networks (Vol. 1, pp. 242-247). IEEE.

Min Max control of nonlinear systems using Universal Learning Networks. / Chen, Hongping; Hirasawa, Kotaro; Hu, Jinglu; Murata, Junichi.

Proceedings of the International Joint Conference on Neural Networks. Vol. 1 IEEE, 2000. p. 242-247.

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

Chen, H, Hirasawa, K, Hu, J & Murata, J 2000, Min Max control of nonlinear systems using Universal Learning Networks. in Proceedings of the International Joint Conference on Neural Networks. vol. 1, IEEE, pp. 242-247, International Joint Conference on Neural Networks (IJCNN'2000), Como, Italy, 7/24/00.
Chen H, Hirasawa K, Hu J, Murata J. Min Max control of nonlinear systems using Universal Learning Networks. In Proceedings of the International Joint Conference on Neural Networks. Vol. 1. IEEE. 2000. p. 242-247
Chen, Hongping ; Hirasawa, Kotaro ; Hu, Jinglu ; Murata, Junichi. / Min Max control of nonlinear systems using Universal Learning Networks. Proceedings of the International Joint Conference on Neural Networks. Vol. 1 IEEE, 2000. pp. 242-247
@inproceedings{909c7b73484e40aca2587e76b1f1c00d,
title = "Min Max control of nonlinear systems using Universal Learning Networks",
abstract = "A Min Max robust control method is proposed for nonlinear systems based on the use of the higher order derivatives calculation of Universal Learning Networks (ULNs). An extended criterion function containing sensitivity terms is considered for controller design and the criterion function is evaluated at several specific operating points corresponding to certain system parameters. The ULNs learning is then performed in such a way that, at each step, it minimizes the worst criterion function among several operating points. It is found that the proposed control method is less time-consuming in the ULNs learning and a obtained controller has better performance than the conventional methods.",
author = "Hongping Chen and Kotaro Hirasawa and Jinglu Hu and Junichi Murata",
year = "2000",
language = "English",
volume = "1",
pages = "242--247",
booktitle = "Proceedings of the International Joint Conference on Neural Networks",
publisher = "IEEE",

}

TY - GEN

T1 - Min Max control of nonlinear systems using Universal Learning Networks

AU - Chen, Hongping

AU - Hirasawa, Kotaro

AU - Hu, Jinglu

AU - Murata, Junichi

PY - 2000

Y1 - 2000

N2 - A Min Max robust control method is proposed for nonlinear systems based on the use of the higher order derivatives calculation of Universal Learning Networks (ULNs). An extended criterion function containing sensitivity terms is considered for controller design and the criterion function is evaluated at several specific operating points corresponding to certain system parameters. The ULNs learning is then performed in such a way that, at each step, it minimizes the worst criterion function among several operating points. It is found that the proposed control method is less time-consuming in the ULNs learning and a obtained controller has better performance than the conventional methods.

AB - A Min Max robust control method is proposed for nonlinear systems based on the use of the higher order derivatives calculation of Universal Learning Networks (ULNs). An extended criterion function containing sensitivity terms is considered for controller design and the criterion function is evaluated at several specific operating points corresponding to certain system parameters. The ULNs learning is then performed in such a way that, at each step, it minimizes the worst criterion function among several operating points. It is found that the proposed control method is less time-consuming in the ULNs learning and a obtained controller has better performance than the conventional methods.

UR - http://www.scopus.com/inward/record.url?scp=0033685046&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0033685046&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:0033685046

VL - 1

SP - 242

EP - 247

BT - Proceedings of the International Joint Conference on Neural Networks

PB - IEEE

ER -