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Dynamic memory event-triggered interval type-2 fuzzy RBFNN control for multi-agent systems under false data injection attacks

Author

Listed:
  • Li, Xin
  • He, Dakuo
  • Zhang, Qiang
  • Liu, Hailong

Abstract

This paper investigates an adaptive neural network event-triggered control problem for cooperative-competitive non-affine multi-agent systems (MASs) with input saturation under false data injection (FDI) attacks. Different from existing results, the two-channel smooth dynamic memory event-triggered mechanism is proposed, which not only stores historical information of dynamic variables to increase the triggering interval, but also uses smooth segmentation functions to circumvent the limitations caused by discontinuous trigger signals. Furthermore, the interval type-2 fuzzy radial basis function neural network with lower and upper membership functions is applied to MASs under FDI attacks, which relaxes the deterministic constraints on membership functions thus effectively handling nonlinear features. On this basis, a new coordinate transformation and auxiliary function are designed to mitigate the effects of FDI attacks and input saturation on system robustness, respectively. Finally, the Lyapunov stability theory is used to demonstrate the bipartite consensus performance of MASs, and all signals in the closed-loop system are bounded. The comparative simulation experiment of MASs in multiple RLC circuit systems verifies the rationality of the proposed method.

Suggested Citation

  • Li, Xin & He, Dakuo & Zhang, Qiang & Liu, Hailong, 2026. "Dynamic memory event-triggered interval type-2 fuzzy RBFNN control for multi-agent systems under false data injection attacks," Applied Mathematics and Computation, Elsevier, vol. 508(C).
    • Handle: RePEc:eee:apmaco:v:508:y:2026:i:c:s0096300325003200
    DOI: 10.1016/j.amc.2025.129594
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    References listed on IDEAS

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    1. Han, Chenglin & Qin, Kaiyu & Shi, Mengji & Liu, Yan & Li, Weihao & Lin, Boxian, 2024. "Prescribed time bipartite output consensus tracking for heterogeneous multi-agent systems with external disturbances," Applied Mathematics and Computation, Elsevier, vol. 469(C).
    2. Li, Hongchao & Niu, Guowei & Chen, Yining, 2024. "Fixed-time consensus of leader-following multi-agent systems subject to failed follower: Reconstructed topology approach," Applied Mathematics and Computation, Elsevier, vol. 482(C).
    3. An, Yaxin & Liu, Yongchao & Wang, Haiyu, 2025. "Dual-channel triggered fuzzy adaptive output feedback control for uncertain nonlinear systems with deception attacks," Chaos, Solitons & Fractals, Elsevier, vol. 191(C).
    4. Zhao, Jipeng & Yang, Guang-Hong, 2023. "Fuzzy adaptive secure tracking control against unknown false data injection attacks for uncertain nonlinear systems with input quantization," Applied Mathematics and Computation, Elsevier, vol. 437(C).
    5. Zhao, Jia-Yu & Zhao, Tao, 2025. "Improving disturbance rejection and tracking performance of interval type-2 T-S fuzzy systems with time-varying delay," Applied Mathematics and Computation, Elsevier, vol. 489(C).
    6. Anand, Amit & Guha, Dipayan & Purwar, Shubhi, 2024. "Adaptive consensus control of leader-follower multi-agent system with actuator deception attacks," Chaos, Solitons & Fractals, Elsevier, vol. 187(C).
    7. Nie, Liduo & Wang, Xin, 2024. "Neural-network-based adaptive control of strict-feedback nonlinear systems with actuator faults: Event-triggered communications strategy," Chaos, Solitons & Fractals, Elsevier, vol. 181(C).
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