Direct adaptive robust NN control for a class of discrete-time nonlinear strict-feedback SISO systems |
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Authors: | Guo-Xing Wen Yan-Jun Liu C L Philip Chen |
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Affiliation: | 1. School of Sciences, Liaoning University of Technology, 121001, Jinzhou, Liaoning, China 2. Faculty of Science and Technology, University of Macau, Macau, Av. Padre Tomás Pereira, S.J., Taipa, Macau, S.A.R., China
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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. |
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