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Predefined-time neural network adaptive control for fractional-order multi-agent systems under deception attacks

Author

Listed:
  • Bi, Wenshan
  • Zhao, Luyao
  • Zhao, Zhihong
  • Basin, Michael V.
  • Sui, Shuai

Abstract

This paper addresses the predefined-time NN adaptive output feedback consensus control problem for FOMASs with unmeasurable states and sensor deception attacks. Firstly, NNs are employed to approximate the unknown nonlinear functions in the system, and an estimator is designed to detect sensor attacks. Based on this, a NN observer is proposed to estimate the system states affected by sensor attacks. Furthermore, combined with the predefined-time theory and fractional-order DSC technique, a predefined-time NN-based adaptive consensus control strategy is designed, which effectively mitigates the impact of sensor attacks on system performance. It is proven that the designed control approach ensures semi-global practical predefined-time stability (SGPPTS) of the controlled system while ensuring consensus between all followers and the leader. Ultimately, the effectiveness and superiority of the presented control issue are verified by simulation results.

Suggested Citation

  • Bi, Wenshan & Zhao, Luyao & Zhao, Zhihong & Basin, Michael V. & Sui, Shuai, 2026. "Predefined-time neural network adaptive control for fractional-order multi-agent systems under deception attacks," Chaos, Solitons & Fractals, Elsevier, vol. 207(C).
    • Handle: RePEc:eee:chsofr:v:207:y:2026:i:c:s0960077926001426
    DOI: 10.1016/j.chaos.2026.118001
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