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Finite-Time Neural Network Event-Triggered Dynamic Surface Control for Nonlinear Pure-Feedback Systems

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Recent Advances in Intelligent Engineering

Part of the book series: Topics in Intelligent Engineering and Informatics ((TIEI,volume 14))

Abstract

This chapter investigates the finite-time neural network event-triggered control issue for a class of nonlinear pure-feedback systems. The dynamic surface control technique is adopted to address the issue of “explosion of complexity” in the backstepping recursive design. Based on an event-triggered mechanism and the approximation property of neural networks, virtual and actual control signals are designed. Under the theoretical framework of finite-time stability, a novel neural network event-triggered dynamic surface control strategy is proposed. The presented control strategy can guarantee that the closed-loop system is semi-globally practically finite-time stable, and the tracking error converges to a small residual set in a finite time. Finally, the effectiveness of theoretical results is verified by means of simulation studies.

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Acknowledgements

This work was supported in part by the National Natural Science Foundation of China (61627901, 61873311), and the 111 Project (B16014).

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Authors and Affiliations

  1. Research Institute of Intelligent Control and Systems, Harbin Institute of Technology, Harbin, 150080, China

    Jianbin Qiu, Kangkang Sun & Huijun Gao

Authors
  1. Jianbin Qiu
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  2. Kangkang Sun
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  3. Huijun Gao
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Corresponding author

Correspondence to Jianbin Qiu .

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Editors and Affiliations

  1. Obuda University, Budapest, Hungary

    Levente Kovács

  2. Óbuda University, Budapest, Hungary

    Tamás Haidegger

  3. Óbuda University, Budapest, Hungary

    Anikó Szakál

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Qiu, J., Sun, K., Gao, H. (2020). Finite-Time Neural Network Event-Triggered Dynamic Surface Control for Nonlinear Pure-Feedback Systems. In: Kovács, L., Haidegger, T., Szakál, A. (eds) Recent Advances in Intelligent Engineering. Topics in Intelligent Engineering and Informatics, vol 14. Springer, Cham. https://doi.org/10.1007/978-3-030-14350-3_2

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