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Robust adaptive NN control for a class of uncertain discrete-time nonlinear MIMO systems

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Abstract

A robust adaptive NN output feedback control is proposed to control a class of uncertain discrete-time nonlinear multi-input–multi-output (MIMO) systems. The high-order neural networks are utilized to approximate the unknown nonlinear functions in the systems. Compared with the previous research for discrete-time MIMO systems, robustness of the proposed adaptive algorithm is obvious improved. Using Lyapunov stability theorem, the results show all the signals in the closed-loop system are semi-globally uniformly ultimately bounded, and the tracking errors converge to a small neighborhood of zero by choosing the design parameters appropriately.

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Acknowledgments

The authors would like to thank the valuable comments and also appreciate the constructive suggestions from the anonymous referees. This research was supported by the Natural Science Foundation of China under Grant 61074014, 61104017, and 60874056; The Natural Science Foundation of Liaoning Province under Grant 20102095; Program for Liaoning Excellent Talents in University under grant LJQ2011064.

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Authors and Affiliations

  1. School of Sciences, Liaoning University of Technology, Jinzhou, 121001, Liaoning, China

    Yang Cui & Yan-Jun Liu

  2. School of Chemical and Environmental Engineering, Liaoning University of Technology, Jinzhou, 121001, Liaoning, China

    Dong-Juan Li

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  1. Yang Cui
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  2. Yan-Jun Liu
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  3. Dong-Juan Li
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Correspondence to Yan-Jun Liu.

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Cui, Y., Liu, YJ. & Li, DJ. Robust adaptive NN control for a class of uncertain discrete-time nonlinear MIMO systems. Neural Comput & Applic 22, 747–754 (2013). https://doi.org/10.1007/s00521-011-0766-4

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  • DOI: https://doi.org/10.1007/s00521-011-0766-4

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