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Research Article | Open Access

Backstepping-sliding-mode-based prescribed-time control for a class of nonlinear systems with disturbances

Litong Zhou1Lichao Feng1,2,3( )Nan Ji2Ruicheng Zhang1
College of Electrical Engineering, North China University of Science and Technology, Tangshan 063210, China
College of Science, North China University of Science and Technology, Tangshan 063210, China
Hebei Key Laboratory of Data Science and Application, North China University of Science and Technology, Tangshan 063210, China
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Abstract

This article addresses the prescribed-time stability (PTS) problem for a class of nonlinear strict-feedback systems subject to unknown disturbances. Herein, a novel backstepping-sliding-mode (BSM) composite control structure is proposed: a dynamic time-varying sliding-mode surface is integrated into the final step of the backstepping design, effectively suppressing matched disturbances and simplifying the implementation complexity. Specifically speaking, 1) for systems with known nonlinear terms, time-varying gains are directly embedded within the backstepping virtual control laws to achieve PTS; 2) for the case of unknown nonlinear terms, by combining adaptive laws with the BSM framework, the controlled system is guaranteed to converge within the prescribed-time and maintain globally stability thereafter. Moreover, the prescribed time T p is designer-prespecified and does not depend on initial conditions or controller gains. Finally, two numerical simulation examples validate the convergence performance and robustness of the proposed approaches.

CLC number: 93C10, 93D05

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AIMS Mathematics
Pages 22791-22816

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Cite this article:
Zhou L, Feng L, Ji N, et al. Backstepping-sliding-mode-based prescribed-time control for a class of nonlinear systems with disturbances. AIMS Mathematics, 2025, 10(10): 22791-22816. https://doi.org/10.3934/math.20251013

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Received: 19 July 2025
Revised: 17 September 2025
Accepted: 24 September 2025
Published: 09 October 2025
©2025 the Author(s), licensee AIMS Press.

This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0)