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Predefined‐time adaptive neural dynamic surface tracking control for high‐order nonlinear switched systems

Meng, Zhu, Ma, Jiawei, Wang, Huanqing·2025·Asian Journal of Control
DOI 10.1002/asjc.3436
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Abstract

In this article, the issue of adaptive predefined‐time control for high‐order switched systems is researched. Neural networks (NNs) are introduced to approximate the uncertain nonlinear functions. In particular, a novel predefined‐time convergence filter is proposed to refrain from the problem of repeated differentiation of virtual controllers. On the basis of the backstepping recursion technique and the common Lyapunov function (CLF) approach, a neural adaptive predefined‐time dynamic surface control (DSC) scheme is proposed that can demonstrate all the signals in closed‐loop systems are bounded and the tracking error can converge to a small area near zero within predefined time. The simulation results illustrate the effectiveness of the proposed control scheme.

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Metadata

Authors
Meng, Z., Ma, J., & Wang, H.
Venue
Asian Journal of Control
Publisher
Wiley
Published
2024-06-27
Volume
27
Issue
1
Pages
343-356
ISSN
  • 1561-8625
  • 1934-6093
DOI
10.1002/asjc.3436
Cited by
2
References
2
Language
en
Licence
onlinelibrary.wiley.com/termsAndConditions#vor (vor)
Funders
National Natural Science Foundation of China (62173046)

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CoChat. (2026). Citation check: Predefined‐time adaptive neural dynamic surface tracking control for high‐order nonlinear switched systems. https://app.cochat.ai/papers/10.1002/asjc.3436
The paper
Meng, Z., Ma, J., & Wang, H. (2025). Predefined‐time adaptive neural dynamic surface tracking control for high‐order nonlinear switched systems. Asian Journal of Control. https://doi.org/10.1002/asjc.3436