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Proceedings Paper

Nonlinear adaptive inverse control via the unified model neural network
Author(s): Jin-Tsong Jeng; Tsu-Tian Lee
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Paper Abstract

In this paper, we propose a new nonlinear adaptive inverse control via a unified model neural network. In order to overcome nonsystematic design and long training time in nonlinear adaptive inverse control, we propose the approximate transformable technique to obtain a Chebyshev Polynomials Based Unified Model (CPBUM) neural network for the feedforward/recurrent neural networks. It turns out that the proposed method can use less training time to get an inverse model. Finally, we apply this proposed method to control magnetic bearing system. The experimental results show that the proposed nonlinear adaptive inverse control architecture provides a greater flexibility and better performance in controlling magnetic bearing systems.

Paper Details

Date Published: 22 March 1999
PDF: 10 pages
Proc. SPIE 3722, Applications and Science of Computational Intelligence II, (22 March 1999); doi: 10.1117/12.342869
Show Author Affiliations
Jin-Tsong Jeng, Hwa-Hsia College of Technology and Commerce (Taiwan)
Tsu-Tian Lee, National Taiwan Univ. of Science and Technology (Taiwan)

Published in SPIE Proceedings Vol. 3722:
Applications and Science of Computational Intelligence II
Kevin L. Priddy; Paul E. Keller; David B. Fogel; James C. Bezdek, Editor(s)

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