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Cited by 317

The DOI matches its Crossref record; 28 of the 28 references it cites were found in a registry or an index.

Finite-Time Adaptive Control for a Class of Nonlinear Systems With Nonstrict Feedback Structure

Sun, Yumei, Chen, Bing, Lin, Chong, Wang, Honghong·2018·IEEE Transactions on Cybernetics

Abstract

This paper focuses on finite-time adaptive neural tracking control for nonlinear systems in nonstrict feedback form. A semiglobal finite-time practical stability criterion is first proposed. Correspondingly, the finite-time adaptive neural control strategy is given by using this criterion. Unlike the existing results on adaptive neural/fuzzy control, the proposed adaptive neural controller guarantees that the tracking error converges to a sufficiently small domain around the origin in finite time, and other closed-loop signals are bounded. At last, two examples are used to test the validity of our results.

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Metadata

Authors
Sun, Y., Chen, B., Lin, C., & Wang, H.
Venue
IEEE Transactions on Cybernetics
Published
2018-10
Volume
48
Issue
10
Pages
2774-2782
ISSN
  • 2168-2267 (ISSN-L)
  • 2168-2275
OpenAlex
W2755590954
Cited by
317
References
28
FWCI
16.01
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Finite-Time Adaptive Control for a Class of Nonlinear Systems With Nonstrict Feedback Structure. https://app.cochat.ai/papers/10.1109/tcyb.2017.2749511
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Sun, Y., Chen, B., Lin, C., & Wang, H. (2018). Finite-Time Adaptive Control for a Class of Nonlinear Systems With Nonstrict Feedback Structure. IEEE Transactions on Cybernetics. https://doi.org/10.1109/tcyb.2017.2749511

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