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

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

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

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 languageEnglish
Title of host publicationIEEE World Congress on Computational Intelligence
Editors Anon
PublisherIEEE
Pages2453-2458
Number of pages6
Volume3
Publication statusPublished - 1998
EventProceedings of the 1998 IEEE International Joint Conference on Neural Networks. Part 1 (of 3) - Anchorage, AK, USA
Duration: May 4 1998May 9 1998

Other

OtherProceedings of the 1998 IEEE International Joint Conference on Neural Networks. Part 1 (of 3)
CityAnchorage, AK, USA
Period5/4/985/9/98

All Science Journal Classification (ASJC) codes

  • Software

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  • Cite this

    Hu, J., Hirasawa, K., Murata, J., Ohbayashi, M., & Kumamaru, K. (1998). Adaptive control of nonlinear black-box systems based on Universal Learning Networks. In Anon (Ed.), IEEE World Congress on Computational Intelligence (Vol. 3, pp. 2453-2458). IEEE.