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Direct adaptive robust NN control for a class of discrete-time nonlinear strict-feedback SISO systems.
- Source :
-
Neural Computing & Applications . Sep2012, Vol. 21 Issue 6, p1423-1431. 9p. 4 Graphs. - Publication Year :
- 2012
-
Abstract
- In this paper, a direct adaptive neural network control algorithm based on the backstepping technique is proposed for a class of uncertain nonlinear discrete-time systems in the strict-feedback form. The neural networks are utilized to approximate unknown functions, and a stable adaptive neural network controller is synthesized. The fact that all the signals in the closed-loop system are semi-globally uniformly ultimately bounded is proven and the tracking error can converge to a small neighborhood of zero by choosing the design parameters appropriately. Compared with the previous research for discrete-time systems, the proposed algorithm improves the robustness of the systems. A simulation example is employed to illustrate the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 09410643
- Volume :
- 21
- Issue :
- 6
- Database :
- Academic Search Index
- Journal :
- Neural Computing & Applications
- Publication Type :
- Academic Journal
- Accession number :
- 78638999
- Full Text :
- https://doi.org/10.1007/s00521-011-0596-4