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Control of magnetic bearing systems via the Chebyshev polynomial-based unified model (CPBUM) neural network

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2 Author(s)
Jin-Tsong Jeng ; Dept. of Electron. Eng., Hwa-Hsia Coll. of Technol. & Commerce, Taipei, Taiwan ; Tsu-Tian Lee,

A Chebyshev polynomial-based unified model (CPBUM) neural network is introduced and applied to control a magnetic bearing systems. First, we show that the CPBUM neural network not only has the same capability of universal approximator, but also has faster learning speed than conventional feedforward/recurrent neural network. It turns out that the CPBUM neural network is more suitable in the design of controller than the conventional feedforward/recurrent neural network. Second, we propose the inverse system method, based on the CPBUM neural networks, to control a magnetic bearing system. The proposed controller has two structures; namely, off-line and on-line learning structures. We derive a new learning algorithm for each proposed structure. The experimental results show that the proposed neural network architecture provides a greater flexibility and better performance in controlling magnetic bearing systems

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Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on  (Volume:30 ,  Issue: 1 )