Direct adaptive robust NN control for a class of discrete-time nonlinear strict-feedback SISO systems

Authors:
Guo-Xing Wen;Yan-Jun Liu;C. L. Philip Chen
Affiliations:
Liaoning University of Technology, School of Sciences, 121001, Jinzhou, Liaoning, China;Liaoning University of Technology, School of Sciences, 121001, Jinzhou, Liaoning, China;University of Macau, Faculty of Science and Technology, Macau, Av. Padre Tomás Pereira, S.J., Taipa, Macau, S.A.R., China
Venue:
Neural Computing and Applications - Special Issue on LSMS2010 and ICSEE 2010
Year:
2012

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Output feedback adaptive robust NN control for a class of nonlinear discrete-time systems

ISNN'13 Proceedings of the 10th international conference on Advances in Neural Networks - Volume Part II

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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.