Please use this identifier to cite or link to this item:
|Title:||Adaptive neural networks control for a class of pure-feedback systems in discrete-time|
|Authors:||Ge, S.S. |
|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|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on Dec 11, 2017
checked on Dec 16, 2017
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.