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Neural network-based adaptive fault-tolerant control for strict-feedback nonlinear systems with input dead zone and saturation

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

Standard

Neural network-based adaptive fault-tolerant control for strict-feedback nonlinear systems with input dead zone and saturation. / Kharrat, Mohamed; Krichen, Moez; Alhazmi, Hadil et al.
In: Journal of the Franklin Institute, Vol. 362, No. 2, 107471, 01.2025.

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

Harvard

Kharrat, M, Krichen, M, Alhazmi, H & Mercorelli, P 2025, 'Neural network-based adaptive fault-tolerant control for strict-feedback nonlinear systems with input dead zone and saturation', Journal of the Franklin Institute, vol. 362, no. 2, 107471. https://doi.org/10.1016/j.jfranklin.2024.107471

APA

Kharrat, M., Krichen, M., Alhazmi, H., & Mercorelli, P. (2025). Neural network-based adaptive fault-tolerant control for strict-feedback nonlinear systems with input dead zone and saturation. Journal of the Franklin Institute, 362(2), Article 107471. https://doi.org/10.1016/j.jfranklin.2024.107471

Vancouver

Kharrat M, Krichen M, Alhazmi H, Mercorelli P. Neural network-based adaptive fault-tolerant control for strict-feedback nonlinear systems with input dead zone and saturation. Journal of the Franklin Institute. 2025 Jan;362(2):107471. doi: 10.1016/j.jfranklin.2024.107471

Bibtex

@article{c29ec2a4dd81407b8cf4d3babf32b645,
title = "Neural network-based adaptive fault-tolerant control for strict-feedback nonlinear systems with input dead zone and saturation",
abstract = "This study investigates the issue of adaptive fault-tolerant neural control in strict-feedback nonlinear systems. The system is subjected to actuator faults, dead-zone and saturation. To model the unknown functions, radial basis function neural networks (RBFNN) are employed. The proposed approach utilizes a backstepping technique to formulate an adaptive fault-tolerant controller, drawing upon the Lyapunov stability theory and the approximation capabilities of RBFNN. The resultant controller guarantees the boundedness of all signals in the closed-loop system, ensuring precise tracking of the reference signal by the system output with a small, bounded error. Finally, simulation results are provided to illustrate the efficacy of the proposed strategy in addressing actuator faults, dead-zone, and saturation.",
keywords = "Actuator fault, Adaptive control, Dead-zone, Lyapunov function, Nonlinear system, One-link manipulator, Saturation, Engineering",
author = "Mohamed Kharrat and Moez Krichen and Hadil Alhazmi and Paolo Mercorelli",
note = "Publisher Copyright: {\textcopyright} 2024",
year = "2025",
month = jan,
doi = "10.1016/j.jfranklin.2024.107471",
language = "English",
volume = "362",
journal = "Journal of the Franklin Institute",
issn = "0016-0032",
publisher = "Elsevier Ltd",
number = "2",

}

RIS

TY - JOUR

T1 - Neural network-based adaptive fault-tolerant control for strict-feedback nonlinear systems with input dead zone and saturation

AU - Kharrat, Mohamed

AU - Krichen, Moez

AU - Alhazmi, Hadil

AU - Mercorelli, Paolo

N1 - Publisher Copyright: © 2024

PY - 2025/1

Y1 - 2025/1

N2 - This study investigates the issue of adaptive fault-tolerant neural control in strict-feedback nonlinear systems. The system is subjected to actuator faults, dead-zone and saturation. To model the unknown functions, radial basis function neural networks (RBFNN) are employed. The proposed approach utilizes a backstepping technique to formulate an adaptive fault-tolerant controller, drawing upon the Lyapunov stability theory and the approximation capabilities of RBFNN. The resultant controller guarantees the boundedness of all signals in the closed-loop system, ensuring precise tracking of the reference signal by the system output with a small, bounded error. Finally, simulation results are provided to illustrate the efficacy of the proposed strategy in addressing actuator faults, dead-zone, and saturation.

AB - This study investigates the issue of adaptive fault-tolerant neural control in strict-feedback nonlinear systems. The system is subjected to actuator faults, dead-zone and saturation. To model the unknown functions, radial basis function neural networks (RBFNN) are employed. The proposed approach utilizes a backstepping technique to formulate an adaptive fault-tolerant controller, drawing upon the Lyapunov stability theory and the approximation capabilities of RBFNN. The resultant controller guarantees the boundedness of all signals in the closed-loop system, ensuring precise tracking of the reference signal by the system output with a small, bounded error. Finally, simulation results are provided to illustrate the efficacy of the proposed strategy in addressing actuator faults, dead-zone, and saturation.

KW - Actuator fault

KW - Adaptive control

KW - Dead-zone

KW - Lyapunov function

KW - Nonlinear system

KW - One-link manipulator

KW - Saturation

KW - Engineering

UR - http://www.scopus.com/inward/record.url?scp=85212536130&partnerID=8YFLogxK

U2 - 10.1016/j.jfranklin.2024.107471

DO - 10.1016/j.jfranklin.2024.107471

M3 - Journal articles

AN - SCOPUS:85212536130

VL - 362

JO - Journal of the Franklin Institute

JF - Journal of the Franklin Institute

SN - 0016-0032

IS - 2

M1 - 107471

ER -

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