1. Direct adaptive NN control for a class of discrete-time nonlinear strict-feedback systems
- Author
-
Liu, Yan-Jun, Wen, Guo-Xing, and Tong, Shao-Cheng
- Subjects
- *
ADAPTIVE control systems , *DISCRETE-time systems , *NONLINEAR systems , *FEEDBACK control systems , *ARTIFICIAL neural networks , *COMPUTER algorithms , *ERRORS - 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]
- Published
- 2010
- Full Text
- View/download PDF