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Intelligence computation based on adaptive tracking design for a class of non-linear discrete-time systems

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Abstract

In this article, a direct adaptive neural networks control algorithm is presented for a class of SISO discrete-time systems with non-symmetric dead-zone. The property of the dead-zone is discretized. Mean value theorem is used to transform the systems into a special form. The unknown functions in the input–output model are approximated using the radial basis function neural networks. Compared with the results for the discrete non-symmetric dead-zone, this article presents a new algorithm to reduce the computational burden. Lyapunov analysis method is utilized to prove that all the signals in the closed-loop systems are semi-global uniformly ultimately bounded. The tracking error is proved to converge to a small set around the zero. A simulation example provided to illustrate the effectiveness of the control schemes.

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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 and 61174017; Supported by Program for Liaoning Excellent Talents in University under grant LJQ2011064 and LJQ2011062.

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Authors and Affiliations

  1. College of Science, Liaoning University of Technology, Jinzhou, 121001, Liaoning, China

    Lei Liu & 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. Lei Liu
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  2. Yan-Jun Liu
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Correspondence to Yan-Jun Liu.

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Liu, L., Liu, YJ. & Li, DJ. Intelligence computation based on adaptive tracking design for a class of non-linear discrete-time systems. Neural Comput & Applic 23, 1351–1357 (2013). https://doi.org/10.1007/s00521-012-1080-5

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  • DOI: https://doi.org/10.1007/s00521-012-1080-5

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