Distributed Event-Triggered Control of Networked Strict-Feedback Systems via Intermittent State Feedback

  • Libei SUN
  • , Xiucai HUANG
  • , Yongduan SONG*
  • *Corresponding author for this work

Research output: Journal PublicationsJournal Article (refereed)peer-review

55 Citations (Scopus)

Abstract

It poses technical difficulty to achieve stable tracking even for single mismatched nonlinear strict-feedback systems when intermittent state feedback is utilized. The underlying problem becomes even more complicated if such systems are networked with directed communication and state-triggering setting. In this work, we present a fully distributed adaptive tracking control scheme for multiple agent systems in strict-feedback form using triggered state from the agent itself and the triggered states from the neighbor agents. To circumvent the non-differentiability issue of virtual controllers stemming from state-triggering, we first develop a distributed continuous control scheme under regular state feedback, upon which we construct the distributed event-triggered control scheme by replacing the states in the preceding scheme with the triggered ones. Several useful lemmas are introduced to allow the stability condition to be established with such replacement, ensuring that all the closed-loop signals are semiglobally uniformly ultimately bounded, with the output tracking error converging to a residual set around zero. Besides, with proper choices of the design parameters, the tracking performance in the mean square sense can be improved. Numerical simulation verifies the benefits and efficiency of the proposed method.
Original languageEnglish
Pages (from-to)5142-5149
Number of pages8
JournalIEEE Transactions on Automatic Control
Volume68
Issue number8
Early online date28 Oct 2022
DOIs
Publication statusPublished - Aug 2023
Externally publishedYes

Bibliographical note

Recommended by Associate Editor Z. Kan.
Publisher Copyright:
© 1963-2012 IEEE.

Funding

This work was supported in part by the National Key Research and Development Program of China under Grant 2021ZD0201300, in part by the National Natural Science Foundation of China under Grant 61991400, Grant 61991403, Grant 61860206008, and Grant 61933012, in part by the Fundamental Research Funds for the Central Universities under Project under Grant 2021CDJXKJC001, in part by the Science and Technology Research Program of Chongqing Municipal Education Commission under Grant KJZD-M202100101, and in part by Chongqing Human Resources and Social Security Bureau under Grant cx2021114.

Keywords

  • Adaptive control
  • backstepping
  • directed topology
  • event-triggered
  • multiple agent systems

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