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|Title:||Direct RBF neural network control of a class of discrete-time non-affine nonlinear systems||Authors:||Zhang, J.
|Issue Date:||2002||Citation:||Zhang, J.,Ge, S.S.,Lee, T.H. (2002). Direct RBF neural network control of a class of discrete-time non-affine nonlinear systems. Proceedings of the American Control Conference 1 : 424-429. ScholarBank@NUS Repository.||Abstract:||In this paper, direct adaptive RBF NN control is presented for a class of discrete-time single-input single-output non-affine nonlinear systems. Implicit function theorem is used to prove the existence and uniqueness of the implicit desired feedback control. Based on the input-output model, RBF neural networks are used to emulate the implicit desired feedback control. The closed-loop is proven to be semi-globally uniformly ultimately bounded (SGUUB) if the design parameters are suitably chosen under certain mild conditions. Simulation results show the effectiveness of the direct RBF neural network control.||Source Title:||Proceedings of the American Control Conference||URI:||http://scholarbank.nus.edu.sg/handle/10635/69963||ISSN:||07431619|
|Appears in Collections:||Staff Publications|
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