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

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

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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

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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.

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