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Global dual-channel dynamic event-triggered control of nonlinear strict-feedback systems with prescribed transient performance

  • Zhirong Zhang
  • , Changyun Wen
  • , Yongduan Song*
  • , Long Chen
  • , Gang Feng
  • *Corresponding author for this work

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

Abstract

In this paper, we develop a novel dual-channel dynamic event-triggered control approach (comprising both the sensor-to-controller and controller-to-actuator channels) for parametric uncertain nonlinear strict-feedback systems. This method guarantees prescribed transient performance using backstepping techniques. Notably, implementing a triggering mechanism at the sensor side poses challenges in designing the backstepping control, as it leads to non-differentiable virtual control signals due to the discontinuous nature of the state/output signals received at the controller side. In contrast to existing results in this domain, our approach features three key innovations: (1) instead of yielding semi-global results, our method enables the achievement of global stability and provides clear guidelines for tuning control parameters; (2) contrasting with conventional static event-triggering mechanisms, our dual-channel dynamic event-triggering design generates larger average inter-event intervals; and (3) unlike traditional barrier functions-based prescribed performance control, our framework allows for controller design based on discontinuous event-triggered states. Finally, the effectiveness and advantages of the proposed event-triggered control approach are validated through a numerical case study. © 2025 Elsevier Ltd.
Original languageEnglish
Article number112676
Number of pages10
JournalAutomatica
Volume183
Online published4 Nov 2025
DOIs
Publication statusPublished - Jan 2026

Funding

Prof. Song was a recipient of several competitive research awards from the National Science Foundation, the National Aeronautics and Space Administration, the U.S. Air Force Office, the U.S. Army Research Office, and the U.S. Naval Research Office. He is an IEEE Fellow and has served/been serving as an Associate Editor for several prestigious international journals, including the IEEE Transactions on Automatic Control, IEEE Transactions on Neural Networks and Learning Systems, IEEE Transactions on Intelligent Transportation Systems, IEEE Transactions on Systems, Man and Cybernetics, etc. He is the Editor-in-Chief for IEEE Transactions on Neural Networks and Learning Systems.

Research Keywords

  • Backstepping
  • Dynamic surface control
  • Event-triggered control
  • Prescribed performance control
  • Uncertain nonlinear systems

Publisher's Copyright Statement

  • COPYRIGHT TERMS OF DEPOSITED POSTPRINT FILE: © 2025. This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0/.

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