Please use this identifier to cite or link to this item: https://doi.org/10.1109/ISIC.2007.4450872
Title: Adaptive neural networks control for a class of pure-feedback systems in discrete-time
Authors: Ge, S.S. 
Yang, C.G.
Lee, T.H. 
Issue Date: 2008
Source: Ge, S.S.,Yang, C.G.,Lee, T.H. (2008). Adaptive neural networks control for a class of pure-feedback systems in discrete-time. 22nd IEEE International Symposium on Intelligent Control, ISIC 2007. Part of IEEE Multi-conference on Systems and Control : 126-131. ScholarBank@NUS Repository. https://doi.org/10.1109/ISIC.2007.4450872
Abstract: In this paper, adaptive neural networks (NNs) control is investigated for a class of nonlinear pure-feedback discrete-time systems by prediction. To overcome the difficulty of nonaffine appearance of control input, the pure-feedback system is transformed into an n-step ahead predictor, and then, implicit function theorem is exploited. NN is employed to approximate the unknown function in the control and the resultant control completely avoids controller singularity problem and achieves semi-global-uniformly-ultimately- boundedness (SGUUB) stability of the closed-loop system. The output tracking error is made within a small neighborhood around zero. The effectiveness of the proposed control approach is demonstrated in the simulation results. © 2007 IEEE.
Source Title: 22nd IEEE International Symposium on Intelligent Control, ISIC 2007. Part of IEEE Multi-conference on Systems and Control
URI: http://scholarbank.nus.edu.sg/handle/10635/69203
ISBN: 142440441X
DOI: 10.1109/ISIC.2007.4450872
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