Nonlinear adaptive inverse control via the unified model neural network

Jin-Tsong Jeng; Tsu-Tian Lee

doi:10.1117/12.342869

22 March 1999 Nonlinear adaptive inverse control via the unified model neural network

Jin-Tsong Jeng, Tsu-Tian Lee

Proceedings Volume 3722, Applications and Science of Computational Intelligence II; (1999) https://doi.org/10.1117/12.342869
Event: AeroSense '99, 1999, Orlando, FL, United States

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.

Citation Download Citation

Jin-Tsong Jeng and Tsu-Tian Lee "Nonlinear adaptive inverse control via the unified model neural network", Proc. SPIE 3722, Applications and Science of Computational Intelligence II, (22 March 1999); https://doi.org/10.1117/12.342869

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