Formation control of second-order nonlinear multi-agent systems under sensor and actuator attacks using adaptive neural network

Volume 40, Issue 3, pp 353--367 https://dx.doi.org/10.22436/jmcs.040.03.05

Publication Date: July 09, 2025 Submission Date: March 03, 2025 Revision Date: May 15, 2025 Accteptance Date: June 17, 2025

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Authors

N. Iqbal - Department of Mathematics, College of Science, University of Ha’il, Ha’il 2440, Saudi Arabia. N. Jahangir - Department of Mathematics and Statistics, The University of Lahore, Sargodha 40100, Pakistan. A. U. K. Niazi - Department of Mathematics and Statistics, The University of Lahore, Sargodha 40100, Pakistan. T. Saidani - Center for Scientific Research and Entrepreneurship, Northern Border University, 73213, Arar, Saudi Arabia. A. B. Rajab - Department of Computer Engineering, College of Engineering, Knowledge University, Erbil 44001, Iraq. - Department of Computer Engineering, Al-Kitab University, Altun Kupri, Iraq.

Abstract

In this paper, we present an adaptive leader-follower formation control strategy using neural networks (NN) for a category of second-order nonlinear Multi-Agent Systems with unmodeled dynamics, this approach tackles the complexities arising from sensor and actuator disruptions. Second-order formation control involves the simultaneous regulation of both the positions and velocities of multiple agents in a formation, which inherently introduces additional complexity compared to first-order control. The primary objective is to achieve asymptotic consensus among control system while significantly reducing inter-agent communication and ensuring security against sensor and actuator attacks. The suggested control technique achieves the necessary leader-follower formation through the utilization of Lyapunov stability analysis to ensure that all system errors stay contained inside ultimate boundedness that is semi-global and uniform even under difficult circumstances. Finally, numerical simulations further validate the robustness and effectiveness of the control framework, demonstrating successful formation control despite sensor and actuator attacks.

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Keywords

• Adaptive neural network
• multi-Agent System (MAS)
• Lyapunov stability
• sensor and actuator attack
• framework for leader-follower
• second-order nonlinear dynamics

MSC

•  93C10
•  93B52
•  93B55
•  93C42
•  93A30

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