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Direct adaptive NN control for a class of discrete-time nonlinear strict-feedback systems
- Source :
-
Neurocomputing . Aug2010, Vol. 73 Issue 13-15, p2498-2505. 8p. - Publication Year :
- 2010
-
Abstract
- Abstract: Based on the backstepping technique, a direct adaptive neural network control algorithm is proposed for a class of uncertain nonlinear discrete-time systems in the strict-feedback form. Neural networks are utilized to approximate unknown functions, and a stable adaptive neural backstepping controller is synthesized. It is proven that all the signals in the closed-loop system are semi-globally uniformly ultimately bounded (SGUUB) and the tracking error converges to a small neighborhood of zero by choosing the design parameters appropriately. Compared with the existing results for discrete-time systems, the proposed algorithm needs only less parameters to be adjusted online, therefore, it can reduce online computation burden. A simulation example is employed to illustrate the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 09252312
- Volume :
- 73
- Issue :
- 13-15
- Database :
- Academic Search Index
- Journal :
- Neurocomputing
- Publication Type :
- Academic Journal
- Accession number :
- 52875208
- Full Text :
- https://doi.org/10.1016/j.neucom.2010.06.001