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Cited by 304

The DOI matches its Crossref record; 35 of the 35 references it cites were found in a registry or an index.

Adaptive Fuzzy Control of Nonlinear Systems With Predefined Time and Accuracy

Wang, Qian, Cao, Jinde, Liu, Heng·2022·IEEE Transactions on Fuzzy Systems

Abstract

In this article, an adaptive fuzzy predefined time and accuracy tracking control scheme is proposed for strict-feedback nonlinear systems. A sufficient condition that can be used to determine whether the tracking error converges to a predefined region within a predefined time is first derived based on the definition of predefined time stability. Under the proposed stability criterion, an adaptive fuzzy controller is developed by applying the backstepping technique, and the stability time and convergence accuracy of the tracking error can be set previously, which facilitates the engineering application with requirements for convergence time and accuracy. Especially, to reduce chattering and decrease energy consumptions on the premise of ensuring the stability of the system, an inequality is introduced to construct a continuous term which can replace the sign function. Finally, a practical example is presented to illustrate the feasibility of main results.

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Metadata

Authors
Wang, Q., Cao, J., & Liu, H.
Venue
IEEE Transactions on Fuzzy Systems
Published
2022-12
Volume
30
Issue
12
Pages
5152-5165
ISSN
  • 1063-6706 (ISSN-L)
  • 1941-0034
OpenAlex
W4226103724
Cited by
304
References
35
FWCI
20.61
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Adaptive Fuzzy Control of Nonlinear Systems With Predefined Time and Accuracy. https://app.cochat.ai/papers/10.1109/tfuzz.2022.3169852
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Wang, Q., Cao, J., & Liu, H. (2022). Adaptive Fuzzy Control of Nonlinear Systems With Predefined Time and Accuracy. IEEE Transactions on Fuzzy Systems. https://doi.org/10.1109/tfuzz.2022.3169852

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.