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<p>This paper develops a robust and secure consensus framework for discrete-time fractional-order multi-agent systems functioning under hybrid cyber attacks and switching topologies. Agents are described by Caputo-type nabla fractional-difference dynamics, which capture long-range memory effects and inherent nonlinear interactions. A unified control strategy integrating dynamic event-triggered communication, adaptive coupling, and neural compensation is developed to achieve resilient leader–follower coordination in adversarial time-varying networks. An event-triggering mechanism reduces unnecessary transmissions and enhances communication efficiency, while an adaptive RBF neural estimator compensates for nonlinear disturbances and attack-induced uncertainties in real time. Secure graph-based coupling ensures stability despite intermittent link failures. Closed-loop stability is rigorously established through discrete fractional Lyapunov theory with average dwell-time conditions, guaranteeing Mittag–Leffler convergence of leader–follower errors. Numerical simulations illustrate accurate consensus tracking, a substantial reduction in communication load, and strong resilience against cyber-induced disruptions, validating the effectiveness of the proposed approach.</p>

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Resilient event-triggered consensus control of discrete-time fractional-order multi-agent systems under hybrid cyber attacks

  • 期刊论文

  • 发表日期：

    2026年7月25日

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  • Ammar Alsinai,
  • Azmat Ullah Khan Niazi,
  • Abid Raza

摘要

This paper develops a robust and secure consensus framework for discrete-time fractional-order multi-agent systems functioning under hybrid cyber attacks and switching topologies. Agents are described by Caputo-type nabla fractional-difference dynamics, which capture long-range memory effects and inherent nonlinear interactions. A unified control strategy integrating dynamic event-triggered communication, adaptive coupling, and neural compensation is developed to achieve resilient leader–follower coordination in adversarial time-varying networks. An event-triggering mechanism reduces unnecessary transmissions and enhances communication efficiency, while an adaptive RBF neural estimator compensates for nonlinear disturbances and attack-induced uncertainties in real time. Secure graph-based coupling ensures stability despite intermittent link failures. Closed-loop stability is rigorously established through discrete fractional Lyapunov theory with average dwell-time conditions, guaranteeing Mittag–Leffler convergence of leader–follower errors. Numerical simulations illustrate accurate consensus tracking, a substantial reduction in communication load, and strong resilience against cyber-induced disruptions, validating the effectiveness of the proposed approach.