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Neural Network-Based Finite-Time Control for Stochastic Nonlinear Systems with Input Dead-Zone and Saturation

Research output: Journal contributions › Journal articles › Research › peer-review

Standard

Neural Network-Based Finite-Time Control for Stochastic Nonlinear Systems with Input Dead-Zone and Saturation. / Kharrat, Mohamed; Krichen, Moez; Alhazmi, Hadil et al.
In: Arabian Journal for Science and Engineering, 11.01.2025.

Research output: Journal contributions › Journal articles › Research › peer-review

Harvard

Kharrat, M, Krichen, M, Alhazmi, H & Mercorelli, P 2025, 'Neural Network-Based Finite-Time Control for Stochastic Nonlinear Systems with Input Dead-Zone and Saturation', Arabian Journal for Science and Engineering. https://doi.org/10.1007/s13369-024-09934-2

APA

Kharrat, M., Krichen, M., Alhazmi, H., & Mercorelli, P. (2025). Neural Network-Based Finite-Time Control for Stochastic Nonlinear Systems with Input Dead-Zone and Saturation. Arabian Journal for Science and Engineering. Advance online publication. https://doi.org/10.1007/s13369-024-09934-2

Vancouver

Kharrat M, Krichen M, Alhazmi H, Mercorelli P. Neural Network-Based Finite-Time Control for Stochastic Nonlinear Systems with Input Dead-Zone and Saturation. Arabian Journal for Science and Engineering. 2025 Jan 11. Epub 2025 Jan 11. doi: 10.1007/s13369-024-09934-2

Bibtex

@article{f7351310b47d4ac9ba520fc6d51ba464,
title = "Neural Network-Based Finite-Time Control for Stochastic Nonlinear Systems with Input Dead-Zone and Saturation",
abstract = "The problem of adaptive control for stochastic systems impacted by saturation and dead zone is discussed in this study. Neural networks are incorporated into the design to effectively control the unknown nonlinear functions present in these systems. The non-smooth input saturation and dead-zone nonlinearities are approximated using the non-affine smooth function. Next, the mean-value theorem is applied to derive the affine form. The study develops an adaptive finite-time controller using the backstepping approach, ensuring semi-globally practical finite-time stability for all closed-loop system signals while driving the tracking error to converge within a finite time to a small region around the origin. To illustrate the efficacy of the suggested control strategy, two simulation examples are given.",
keywords = "Dead-zone, Finite-time stability, Nonlinear systems, Saturation, Stochastic systems, Engineering",
author = "Mohamed Kharrat and Moez Krichen and Hadil Alhazmi and Paolo Mercorelli",
note = "Publisher Copyright: {\textcopyright} The Author(s) 2025.",
year = "2025",
month = jan,
day = "11",
doi = "10.1007/s13369-024-09934-2",
language = "English",
journal = "Arabian Journal for Science and Engineering",
issn = "2193-567X",
publisher = "Springer Nature",

}

RIS

TY - JOUR

T1 - Neural Network-Based Finite-Time Control for Stochastic Nonlinear Systems with Input Dead-Zone and Saturation

AU - Kharrat, Mohamed

AU - Krichen, Moez

AU - Alhazmi, Hadil

AU - Mercorelli, Paolo

N1 - Publisher Copyright: © The Author(s) 2025.

PY - 2025/1/11

Y1 - 2025/1/11

N2 - The problem of adaptive control for stochastic systems impacted by saturation and dead zone is discussed in this study. Neural networks are incorporated into the design to effectively control the unknown nonlinear functions present in these systems. The non-smooth input saturation and dead-zone nonlinearities are approximated using the non-affine smooth function. Next, the mean-value theorem is applied to derive the affine form. The study develops an adaptive finite-time controller using the backstepping approach, ensuring semi-globally practical finite-time stability for all closed-loop system signals while driving the tracking error to converge within a finite time to a small region around the origin. To illustrate the efficacy of the suggested control strategy, two simulation examples are given.

AB - The problem of adaptive control for stochastic systems impacted by saturation and dead zone is discussed in this study. Neural networks are incorporated into the design to effectively control the unknown nonlinear functions present in these systems. The non-smooth input saturation and dead-zone nonlinearities are approximated using the non-affine smooth function. Next, the mean-value theorem is applied to derive the affine form. The study develops an adaptive finite-time controller using the backstepping approach, ensuring semi-globally practical finite-time stability for all closed-loop system signals while driving the tracking error to converge within a finite time to a small region around the origin. To illustrate the efficacy of the suggested control strategy, two simulation examples are given.

KW - Dead-zone

KW - Finite-time stability

KW - Nonlinear systems

KW - Saturation

KW - Stochastic systems

KW - Engineering

UR - http://www.scopus.com/inward/record.url?scp=85217547068&partnerID=8YFLogxK

U2 - 10.1007/s13369-024-09934-2

DO - 10.1007/s13369-024-09934-2

M3 - Journal articles

AN - SCOPUS:85217547068

JO - Arabian Journal for Science and Engineering

JF - Arabian Journal for Science and Engineering

SN - 2193-567X

ER -

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Research output: Journal contributions › Journal articles › Research › peer-review

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Research output: Books and anthologies › Collected editions and anthologies › Research

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