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Adaptive event-triggered control for a class of nonlinear systems with periodic disturbances

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Abstract

This paper investigates the adaptive event-triggered control problem for a class of nonlinear systems subject to periodic disturbances. To reduce the communication burden, a reliable relative threshold strategy is proposed. Fourier series expansion and radial basis function neural network are combined into a function approximator to model suitable time-varying disturbed function of known periods in strict-feedback systems. By combining the Lyapunov stability theory and the backstepping technique, the proposed adaptive control approach ensures that all the signals in the closed-loop system are bounded, and the tracking error can be regulated to a compact set around zero in finite time. Finally, simulation results are presented to verify the effectiveness of the theoretical results.

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Acknowledgements

This work was partially supported by National Key R&D Program of China (Grant No. 2018YFB-1700400).

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Authors and Affiliations

  1. School of Automation and Guangdong Province Key Laboratory of Intelligent Decision and Cooperative Control, Guangdong University of Technology, Guangzhou, 510006, China

    Hui Ma, Hongyi Li & Renquan Lu

  2. Science Program, Texas A&M University at Qatar, Doha, 23874, Qatar

    Tingwen Huang

Authors
  1. Hui Ma
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  2. Hongyi Li
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  3. Renquan Lu
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  4. Tingwen Huang
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Correspondence to Hongyi Li.

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Ma, H., Li, H., Lu, R. et al. Adaptive event-triggered control for a class of nonlinear systems with periodic disturbances. Sci. China Inf. Sci. 63, 150212 (2020). https://doi.org/10.1007/s11432-019-2680-1

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  • DOI: https://doi.org/10.1007/s11432-019-2680-1

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