Neuroadptive Practical Prescribed Time Tracking Control of Fully Quantized Uncertain Strict Feedback Systems

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

6 Citations (Scopus)

Abstract

This brief presents a tracking control method for a class of fully quantized uncertain strict feedback systems. When the input and the states are all (fully) quantized, those signals are no longer continuous, making it rather challenging to use traditional backstepping for control design. In this brief, we propose a quantized control scheme with the following characteristics: 1) By introducing dynamic surface control (DSC), the problem of non-differentiation of quantized signal is avoided; 2) A simple rate function is embedded into both the quantized adaptive laws and the controller, ensuring that the tracking error and all the other internal signals converge to an adjustable precision region within predetermined time; and 3) As one of the crucial steps to prove system stability, we exclusively establish a key lemma, with which we show that the error caused by signal replacement is bounded. Numerical simulations are carried out to illustrate the effectiveness of the suggested approach.
Original languageEnglish
Pages (from-to)2144-2148
Number of pages5
JournalIEEE Transactions on Circuits and Systems II: Express Briefs
Volume71
Issue number4
Early online date16 Nov 2023
DOIs
Publication statusPublished - Apr 2024
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2023 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 61991403, Grant 61860206008, Grant 62250710167, Grant 61860206008, Grant 61991400, and Grant 61933012.

Keywords

  • adaptive control
  • dynamic surface control
  • neural networks
  • practical prescribed time control
  • State quantization

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