Robust Adaptive Control for Delayed Fractional-Order Multi-Agent Systems with Unmodeled Cyber-Physical Attacks

Articles in Press

Document Type : Research Paper

Authors
Ammar Alsinai 1
Azmat Ullah Khan Niazi 2
Sundas Asghar 2
Aseel Smerat 3 , 4
1 Department of Computer Science, College of Engineering and Information Technology, Onaizah Colleges, Qassim, Saudi Arabia
2 Department of Mathematics and Statistics, University of Lahore, Sargodha, Pakistan
3 Department of Biosciences, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai, 602105, India
4 Hourani Center for Applied Scientific Research, Al-Ahliyya Amman University, Amman 19328, Jordan
10.22055/jacm.2026.48703.5437
Abstract
Fractional-order multi-agent systems (MASs) with communication delays play a vital role in smart grids, autonomous vehicles, and industrial automation, but their memory-dependent dynamics increase vulnerability to cyber-physical attacks. In particular, malicious sensor and actuator attacks compromise feedback and control signals, threatening stability and consensus. Traditional control methods often require   prior knowledge of attack models, which restricts their effectiveness against unknown and time-varying disturbances. This paper proposes a robust adaptive control framework for delayed fractional-order MASs within the Caputo derivative setting. The method integrates an adaptive estimation law with Caputo-based Lyapunov analysis to counter unmodeled sensor and actuator attacks using only local and neighboring agent data. Stability conditions are rigorously established through a fractional Razumikhin approach. Simulations validate the scheme's resilience under sensor-only, actuator-only, and combined attacks, confirming its ability to maintain consensus, achieve faster convergence, and improve reliability in hostile environments.
Keywords
Fractional-order multi-agent systems
Communication delays
Cyber-physical attacks
Adaptive control
Consensus control
Subjects

Publisher’s Note Shahid Chamran University of Ahvaz remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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Journal of Applied and Computational Mechanics

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Available Online from 22 March 2026