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Predefined‐time adaptive neural dynamic surface tracking control for high‐order nonlinear switched systems
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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Citations
Cites
Adaptive Finite-Time Tracking Control of Nonlinear Systems With Dynamics Uncertainties
Wang, H., Xu, K., & Zhang, H. (2023) · IEEE Transactions on Automatic Control · doi:10.1109/tac.2022.3226703 · Cited by 342
Self-citationAsiri, S., & Liu, D. (2023) · Asian Journal of Control · doi:10.1002/asjc.2858 · Cited by 18
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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
- Cited by
- 2
- References
- 2
- Language
- en
- Funders
- National Natural Science Foundation of China (62173046)
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