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A neural adaptive inverse compensator design method was proposed for a class of nonlinear systems with input unmodeled dynamics based on RBF neural networks. The compensator was designed using two neural networks, one to estimate the input unmodeled dynamics and another to provide adaptive inverse compensation to the input unmodeled dynamics. The method relaxes some rigorous demands to unmodeled dynamics...
Adaptive neural network control is presented for a class of SISO nonlinear systems without a priori knowledge of control direction. A systematic procedure, which relaxes some rigorous restrictions on the plants in the literature at present stage, is developed based on Nussbaum-type function and RBF neural networks. The developed control scheme guarantees global stability of the closed-loop systems.
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