Dual-Channel Event-Triggered Robust Adaptive Control of Strict-Feedback System With Flexible Prescribed Performance

  • Lianhua LI
  • , Kai ZHAO*
  • , Zhirong ZHANG
  • , Yongduan SONG
  • *Corresponding author for this work

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

48 Citations (Scopus)

Abstract

In this note, we present an event-triggered robust adaptive control method with flexible prescribed performance for strict-feedback nonlinear systems. Unlike most existing event-triggered control results with only the inputs being triggered, here we introduce a triggering mechanism into the control law and the parameter estimator simultaneously, so that the communication resources are saved. It is worth noting that under the proposed triggering conditions, there are some challenges and difficulties in directly applying the backstepping technique, as the intermittent (triggering) parameter adaptive law introduces additional sampling errors. To address this issue, a decomposition technique for the event-triggered adaptive law and a new lemma for handling the event error are introduced, with which the execution error is gracefully counteracted with a properly designed compensation unit. Moreover, to ensure the flexible prescribed tracking performance, we incorporate a series of functional transformations into the control design. It is shown that, with the fixed control structure, only by adjusting the key parameters and time-varying function, the proposed control can generate multiple kinds of prescribed performance behaviors, which is more general and flexible than the existing prescribed performance controls. The effectiveness of our control scheme is verified by simulation results.
Original languageEnglish
Pages (from-to)1752-1759
Number of pages8
JournalIEEE Transactions on Automatic Control
Volume69
Issue number3
Early online date30 Oct 2023
DOIs
Publication statusPublished - Mar 2024
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 1963-2012 IEEE.

Funding

This work was supported in part by the National Key Research and Development Program of China under Grant 2022YFB4701400/4701401, and in part by the National Natural Science Foundation of China under Grant 61991400, Grant 61991403, Grant 61860206008, and Grant 61933012.

Keywords

  • Adaptive backstepping control
  • event triggering
  • prescribed performance
  • strict-feedback systems

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