Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/69963
Title: Direct RBF neural network control of a class of discrete-time non-affine nonlinear systems
Authors: Zhang, J. 
Ge, S.S. 
Lee, T.H. 
Issue Date: 2002
Source: 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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