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

A Chebyshev polynomial-based unified model (CPBUM) neural network is introd uced and applied to control a magnetic bearing systems. First, we show that the CPBUM neural network not only has the same capability of universal app roximator, but also has faster learning speed than conventional feedforward /recurrent neural network. It turns out that the CPBUM neural network is mo re suitable in the design of controller than the conventional feedforward/r ecurrent neural network. Second, we proposed the inverse system method, bas ed on the CPBUM neural networks, to control a magnetic bearing system. The proposed controller has two structures; namely, off-line and on-line learni ng structures. We derive a new learning algorithm for each proposed structu re. The experimental results show that the proposed neural network architec ture provides a greater flexibility and better performance in controlling m agnetic bearing systems.