Adaptive control of nonlinear black-box systems based on Universal Learning Networks

Jinglu Hu; Kotaro Hirasawa; Junichi Murata; Masanao Ohbayashi; Kousuke Kumamaru

Adaptive control of nonlinear black-box systems based on Universal Learning Networks

Jinglu Hu^*, Kotaro Hirasawa, Junichi Murata, Masanao Ohbayashi, Kousuke Kumamaru

^*Corresponding author for this work

Research output: Contribution to conference › Paper › peer-review

Abstract

This paper presents an adaptive control scheme for nonlinear black-box systems based on the use of Universal Learning Networks (ULN). A ULN nonlinear controller is constructed in a similar way to linear stochastic control theory. In the obtained ULN controller, some node functions are known, while others are unknown. Each unknown node function is re-parameterized using an adaptive fuzzy model. A robust adaptive algorithm is developed to adjust the unknown parameters in the controller. The effectiveness of the proposed control scheme is examined via numerical simulations.

Original language	English
Pages	2453-2458
Number of pages	6
Publication status	Published - 1998 Jan 1
Externally published	Yes
Event	Proceedings of the 1998 IEEE International Joint Conference on Neural Networks. Part 1 (of 3) - Anchorage, AK, USA Duration: 1998 May 4 → 1998 May 9

Other

Other	Proceedings of the 1998 IEEE International Joint Conference on Neural Networks. Part 1 (of 3)
City	Anchorage, AK, USA
Period	98/5/4 → 98/5/9

ASJC Scopus subject areas

Software

Cite this

Adaptive control of nonlinear black-box systems based on Universal Learning Networks. / Hu, Jinglu; Hirasawa, Kotaro; Murata, Junichi; Ohbayashi, Masanao; Kumamaru, Kousuke.

1998. 2453-2458 Paper presented at Proceedings of the 1998 IEEE International Joint Conference on Neural Networks. Part 1 (of 3), Anchorage, AK, USA.

Research output: Contribution to conference › Paper › peer-review

@conference{6178dc15473e47cc9fecfcfef270bec3,

title = "Adaptive control of nonlinear black-box systems based on Universal Learning Networks",

abstract = "This paper presents an adaptive control scheme for nonlinear black-box systems based on the use of Universal Learning Networks (ULN). A ULN nonlinear controller is constructed in a similar way to linear stochastic control theory. In the obtained ULN controller, some node functions are known, while others are unknown. Each unknown node function is re-parameterized using an adaptive fuzzy model. A robust adaptive algorithm is developed to adjust the unknown parameters in the controller. The effectiveness of the proposed control scheme is examined via numerical simulations.",

author = "Jinglu Hu and Kotaro Hirasawa and Junichi Murata and Masanao Ohbayashi and Kousuke Kumamaru",

year = "1998",

month = jan,

day = "1",

language = "English",

pages = "2453--2458",

note = "Proceedings of the 1998 IEEE International Joint Conference on Neural Networks. Part 1 (of 3) ; Conference date: 04-05-1998 Through 09-05-1998",

}

TY - CONF

T1 - Adaptive control of nonlinear black-box systems based on Universal Learning Networks

AU - Hu, Jinglu

AU - Hirasawa, Kotaro

AU - Murata, Junichi

AU - Ohbayashi, Masanao

AU - Kumamaru, Kousuke

PY - 1998/1/1

Y1 - 1998/1/1

N2 - This paper presents an adaptive control scheme for nonlinear black-box systems based on the use of Universal Learning Networks (ULN). A ULN nonlinear controller is constructed in a similar way to linear stochastic control theory. In the obtained ULN controller, some node functions are known, while others are unknown. Each unknown node function is re-parameterized using an adaptive fuzzy model. A robust adaptive algorithm is developed to adjust the unknown parameters in the controller. The effectiveness of the proposed control scheme is examined via numerical simulations.

AB - This paper presents an adaptive control scheme for nonlinear black-box systems based on the use of Universal Learning Networks (ULN). A ULN nonlinear controller is constructed in a similar way to linear stochastic control theory. In the obtained ULN controller, some node functions are known, while others are unknown. Each unknown node function is re-parameterized using an adaptive fuzzy model. A robust adaptive algorithm is developed to adjust the unknown parameters in the controller. The effectiveness of the proposed control scheme is examined via numerical simulations.

UR - http://www.scopus.com/inward/record.url?scp=0031642355&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0031642355&partnerID=8YFLogxK

M3 - Paper

AN - SCOPUS:0031642355

SP - 2453

EP - 2458

T2 - Proceedings of the 1998 IEEE International Joint Conference on Neural Networks. Part 1 (of 3)

Y2 - 4 May 1998 through 9 May 1998

ER -

Adaptive control of nonlinear black-box systems based on Universal Learning Networks

Abstract

Other

ASJC Scopus subject areas

Other files and links

Fingerprint

Cite this