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Event-triggered neural intelligent control for uncertain nonlinear systems with specified-time guaranteed behaviors

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Abstract

In this paper, an event-triggered neural intelligent control for uncertain nonlinear systems with specified-time guaranteed behaviors is proposed. To cope with constrained communication resources, an event-triggered mechanism using switched thresholds is devised without involving input-to-state stability assumption, such that a better design flexibility and freedom can be provided. In addition, a minimum-learning-parameter-based state observer is developed to online estimate the unavailable states and uncertainties at the same time, which effectively eliminates the issue of learning explosion without sacrificing the identification precision. Furthermore, in pursuit of making a compromise between sampling cost and tracking performance, a modified barrier Lyapunov function based on a time-varying finite-time behavior boundary is constructed in the controller design, which can guarantee that the tracking error converges to a predetermined region within a specified time. Then by introducing the Nussbaum gain technique to handle the unknown control direction, an event-triggered neural output feedback control strategy is synthesized within the framework of dynamic surface control. Meanwhile, with the aid of Lyapunov synthesis, all the signals involved in the closed-loop system are proved to be bounded while Zeno phenomena is circumvented, and system outputs are well within the predefined region. Finally, an application on control design for a micro-electro-mechanical system gyroscope is given to validate the efficiency and superiority of proposed intelligent control scheme.

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Acknowledgements

In behalf of the co-authors, I would like to deliver sincere appreciation to the editors and reviewers for their valuable suggestions on improving the quality of this article. This research has been supported in part by National Natural Science Foundation of China under Grant 61803348, State Key Laboratory of Deep Buried Target Damage under Grant DXMBJJ2019-02, Shanxi Province Science Foundation for Youths under Grant 201701D221123, Youth Academic Leader Program of North University of China under Grant QX201803, Program for the Innovative Talents of Higher Education Institutions of Shanxi, and Shanxi 1331 Project Key Subjects Construction (1331KSC).

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Correspondence to Xingling Shao.

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The authors Shao Xingling, Si Haonan and Zhang Wendong declare that there is no potential conflict of interest with regard to this work.

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Shao, X., Si, H. & Zhang, W. Event-triggered neural intelligent control for uncertain nonlinear systems with specified-time guaranteed behaviors. Neural Comput & Applic 33, 5771–5791 (2021). https://doi.org/10.1007/s00521-020-05357-w

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