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Adaptive NN prescribed performance control design for uncertain switching nonlinear systems with periodically time-varying parameters

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Abstract

This paper addresses a tracking control problem with prespecified accuracy for uncertain switching nonlinear systems under arbitrary switching with periodically time-varying parameters. To approximate the unknown nonlinear functions and unknown periodically time-varying parameters, the radial basis function neural network and Fourier series expansion are introduced, respectively. Compared with the previous results of unknown nonlinear switching system approximation, the upper bounds of the approximation errors are considered for the first time. And then, a new adaptive NN control scheme is constructed by using backstepping technique and common Lyapunov function theory. It can be proved that all the signals in the closed-loop system are semi-globally uniformly ultimately bounded and the tracking error converges to the prescribed neighborhood of zero. Two examples are provided to verify the feasibility and advantages of the proposed approach in this paper.

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Acknowledgements

This work was supported by National Natural Science Foundation of China (61673014), the Natural Foundation of Xianyang Normal University under Grants (12XSYK011), and the Special Scientific Research Plan Project of Shaanxi Provincial Department of Education (21JK0488).

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Authors and Affiliations

  1. School of Mathematics and Statistics, Xidian University, Xi’an, 710071, China

    Xiaoli Yang & Jing Li

  2. School of Mathematics and Information Science, Xianyang Normal University, Xianyang, 712000, Shaanxi, China

    Shuiyan Wu

  3. School of Mathematics and Information Science, Baoji University of Arts and Sciences, Baoji, 721013, China

    Xiaobo Li

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  1. Xiaoli Yang
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  2. Jing Li
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  3. Shuiyan Wu
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  4. Xiaobo Li
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Correspondence to Jing Li or Shuiyan Wu.

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Yang, X., Li, J., Wu, S. et al. Adaptive NN prescribed performance control design for uncertain switching nonlinear systems with periodically time-varying parameters. Neural Comput & Applic 34, 617–629 (2022). https://doi.org/10.1007/s00521-021-06387-8

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  • DOI: https://doi.org/10.1007/s00521-021-06387-8

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