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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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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DOI: https://doi.org/10.1007/978-3-030-14350-3_2
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