Finite-time fuzzy control strategy for nonlinear MASs with actuator faults and deception attacks

Hanen Louati; Rohma Arooj; Azmat Ullah Khan Niazi; Mhassen. E. E. Dalam; Mohammed M. A. Almazah; Aseel Smerat; Hanen Louati; Rohma Arooj; Azmat Ullah Khan Niazi; Mhassen. E. E. Dalam; Mohammed M. A. Almazah; Aseel Smerat

doi:10.3934/math.2025899

AIMS Mathematics

2025, Volume 10, Issue 9: 20113-20139. doi: 10.3934/math.2025899

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Research article

Finite-time fuzzy control strategy for nonlinear MASs with actuator faults and deception attacks

1.
Department of Mathematics, Faculty of Science, Northern Border University, Arar, Saudi Arabia; Hanen.Louati@nbu.edu.sa
2.
Department of Mathematics and Statistics, University of Lahore, Sargodha 40100, Pakistan; aroojrohman@gmail.com, azmatniazi35@gmail.com
3.
Department of Mathematics, College of Sciences and Arts (Muhyil), King Khalid University, Muhyil 61421, Saudi Arabia; mdalam01@kku.edu.sa, mmalmazah@kku.edu.sa
4.
Faculty of Educational Sciences, Al-Ahliyya Amman University, Amman 19328, Jordan; smerat.2020@gmail.com
5.
Department of Biosciences, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai, 602105, India; smerat.2020@gmail.com

Received: 02 June 2025 Revised: 04 August 2025 Accepted: 15 August 2025 Published: 02 September 2025
MSC : 26A33, 34K37

In this paper, we addressed the issue of maintaining a desirable level of performance in the presence of actuator faults and deception attacks in nonlinear multi-agent systems (MASs). These problems are critical for the stability and coordination of MASs that are increasingly used in robotics, autonomous vehicles, and industrial automation. Our aim was to design control strategies that, in the presence of these challenges, guarantee practical finite-time stability and robust tracking performance. To accomplish this, a distributed adaptive fuzzy control scheme based on backstepping was developed. Fuzzy logic systems were utilized to capture the complex system's unknown nonlinearities, while adaptive laws were designed to estimate and mitigate actuator gain and bias faults. A Nussbaum-type function was introduced to address unknown control directions resulting from deception attacks. Stability was verified by the Lyapunov theory. The suggested approach ensured that it was a finite-time stable method, and every signal in the closed loop was found to be semi-globally uniformly eventually bounded. Our control strategy, compared to published approaches, improved convergence time by approximately 38% and tracking accuracy of approximately 35% under the same conditions of simultaneous actuator faults and deception attacks.
- finite-time control,
- multi-agent systems,
- adaptive fuzzy control,
- gain and bias faults,
- deception attacks
Citation: Hanen Louati, Rohma Arooj, Azmat Ullah Khan Niazi, Mhassen. E. E. Dalam, Mohammed M. A. Almazah, Aseel Smerat. Finite-time fuzzy control strategy for nonlinear MASs with actuator faults and deception attacks[J]. AIMS Mathematics, 2025, 10(9): 20113-20139. doi: 10.3934/math.2025899

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Abstract

In this paper, we addressed the issue of maintaining a desirable level of performance in the presence of actuator faults and deception attacks in nonlinear multi-agent systems (MASs). These problems are critical for the stability and coordination of MASs that are increasingly used in robotics, autonomous vehicles, and industrial automation. Our aim was to design control strategies that, in the presence of these challenges, guarantee practical finite-time stability and robust tracking performance. To accomplish this, a distributed adaptive fuzzy control scheme based on backstepping was developed. Fuzzy logic systems were utilized to capture the complex system's unknown nonlinearities, while adaptive laws were designed to estimate and mitigate actuator gain and bias faults. A Nussbaum-type function was introduced to address unknown control directions resulting from deception attacks. Stability was verified by the Lyapunov theory. The suggested approach ensured that it was a finite-time stable method, and every signal in the closed loop was found to be semi-globally uniformly eventually bounded. Our control strategy, compared to published approaches, improved convergence time by approximately 38% and tracking accuracy of approximately 35% under the same conditions of simultaneous actuator faults and deception attacks.

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