2609005303
  • Open Access
  • Article

Event-Triggered Fault-Tolerant Control for Multi-Agent Systems with Polynomial-Form Faults

  • Kangjia Fan,   
  • Xinlei Qin,   
  • Owen Oteng Ranko,   
  • Guochen Pang *,   
  • Chulin Hu

Received: 05 Jul 2026 | Revised: 25 Aug 2026 | Accepted: 25 Sep 2026 | Published: 29 Sep 2026

Abstract

This paper addresses the fault-tolerant control(FTC) problem in multi-agent systems(MAS), with a focus on actuator faults characterized by polynomial forms. The polynomial model effectively captures a variety of complex fault patterns and enables flexible representation of fault signals through polynomial approximation. Based on this model, a state observer is designed to simultaneously estimate the system states, fault signals, and external disturbances. To reduce communication load, an event-triggered mechanism incorporating an open-loop estimator is introduced. By integrating the observer and the event-triggered mechanism, a distributed FTC protocol is developed, which not only mitigates the impact of actuator faults but also attenuates external disturbances, thereby significantly enhancing system stability. Both theoretical analysis and simulation results demonstrate that the proposed scheme ensures satisfactory consensus performance in MAS.

References 

  • 1.

    Xue, S.; Zhao, N.; Wang, L.; et al. Multi‑Agent Self‑Attention Reinforcement Learning for Multi‑USV Hunting Target. Neural Netw. 2025, 189, 107574. https://doi.org/10.1016/j.neunet.2025.107574.

  • 2.

    Liu, C.; Chen, H.; Jiang, B.; et al. Adaptive Reconfigurable Fault‑Tolerant Control of Multi‑USVs with Actuator Magnitude and Rate Faults. IEEE Trans. Ind. Electron. 2025, 72, 8512–8521. https://doi.org/10.1109/tie.2025.3528508.

  • 3.

    Jiao, K.; Chen, J.; Xin, B.; et al. Multiagent Reinforcement Learning with Evolution for Multitarget Tracking by Unmanned Aerial Vehicle Swarm. Appl. Soft Comput. 2025, 181, 113463. https://doi.org/10.1016/j.asoc.2025.113463.

  • 4.

    Zhao, B.; Huo, M.; Li, Z.; et al. Graph‑Based Multi‑Agent Reinforcement Learning for Large‑Scale UAVs Swarm System Control. Aerosp. Sci. Technol. 2024, 150, 109166. https://doi.org/10.1016/j.ast.2024.109166.

  • 5.

    Hou, Y.; Zhao, J.; Zhang, R.; et al. UAV Swarm Cooperative Target Search: A Multi‑Agent Reinforcement Learning Approach. IEEE Trans. Intell. Veh. 2023, 9, 568–578. https://doi.org/10.1109/tiv.2023.3316196.

  • 6.

    Li, Q.; Cui, Y.; Song, T.; et al. Federated Multiagent Actor–Critic Learning Task Offloading in Intelligent Logistics. IEEE Internet Things J. 2023, 10, 11696–11707. https://doi.org/10.1109/jiot.2023.3244783.

  • 7.

    Arishi, A.; Ahuja, P. Multi‑Agent Reinforcement Learning for Truck–Drone Routing in Smart Logistics: A Comprehensive Review. Comput. Electr. Eng. 2025, 127, 110529. https://doi.org/10.1016/j.compeleceng.2025.110529.

  • 8.

    Ren, D.; Pang, G.; Mou, X.; et al. Simultaneous Fault‑Tolerant Consensus Control and Disturbance Suppression for Multi‑Agent Systems with Polynomial Form Fault under Switching Topology. Syst. Control Lett. 2024, 193, 105946. https://doi.org/10.1016/j.sysconle.2024.105946.

  • 9.

    Fan, K.; Pang, G.; Chen, X.; et al. Fault‑Tolerant Control of Multi‑Agent Systems Using a Fourier Series Observer under DoS Attacks and Periodic Intermittent Faults. Syst. Control Lett. 2026, 211, 106397. https://doi.org/10.1016/j.sysconle.2026.106397.

  • 10.

    Wang, Y.; Chen, J.; Wang, F. Observer‑Based Finite‑Horizon H∞ Consensus Control of Multi‑Agent Systems under Multi‑Round‑Robin Scheduling. Intell. Control 2026, 2, 3. https://doi.org/10.53941/ic.2026.100003.

  • 11.

    Liu, L.; Yu, C.; Wang, Y.; et al. Dynamic Formation Tracking and Fault‑Tolerant Control of Multi‑Agent Systems Based on Distance and Topology Reconstruction Methods. ISA Trans. 2025, 165, 15–26. https://doi.org/10.1016/j.isatra.2025.06.006.

  • 12.

    Fan, Q.Y.; Deng, C.; Ge, X.; et al. Distributed Adaptive Fault‑Tolerant Control for Heterogeneous Multiagent Systems with Time‑Varying Communication Delays. IEEE Trans. Syst. Man Cybern. Syst. 2021, 52, 4362–4372. https://doi.org/10.1109/tsmc.2021.3095263.

  • 13.

    Liu, Y.; Liu, S.; Xu, N.; et al. Predictive Fault‑Tolerant Control of an Aero‑Engine Actuator Based on an N‑Step Extended State Observer. Intell. Control 2025, 1, 1. https://doi.org/10.53941/ic.2025.100001.

  • 14.

    Liu, C.; Jiang, B.; Zhang, Y.; et al. Event‑Triggered Fault‑Tolerant Consensus Control of Multiagent Systems with Hybrid Attacks. IEEE Trans. Syst. Man Cybern. Syst. 2025, 55, 5541–5552. https://doi.org/10.1109/tsmc.2025.3571020.

  • 15.

    Wang, Y.; Wang, Z. Distributed Model Free Adaptive Fault‑Tolerant Consensus Tracking Control for Multiagent Systems with Actuator Faults. Inf. Sci. 2024, 664, 120313. https://doi.org/10.1016/j.ins.2024.120313.

  • 16.

    Jiao, Y.; Pang, G.; Mou, X.; et al. Simultaneous Fault‑Tolerant Control and Disturbance Rejection for Systems with Fault in Polynomial Form. Int. J. Robust Nonlinear Control. 2023, 33, 919–932. https://doi.org/10.1002/rnc.6422.

  • 17.

    Zhao, J.; Zhao, H.; Song, Y.; et al. Fast Finite‑Time Consensus Protocol for High‑Order Nonlinear Multi‑Agent Systems Based on Event‑Triggered Communication Scheme. Appl. Math. Comput. 2026, 508, 129631. https://doi.org/10.1016/j.amc.2025.129631.

  • 18.

    Li, L.; Zhao, J.; Ma, D.; et al. Dissipativity‑Based Security Control for Switched Systems with Encryption/Decryption under Switching‑Q‑Learning‑Based Replay‑Attack Detection. Automatica 2025, 179, 112396. https://doi.org/10.1016/j.automatica.2025.112396.

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How to Cite
Fan, K.; Qin, X.; Ranko, O. O.; Pang, G.; Hu, C. Event-Triggered Fault-Tolerant Control for Multi-Agent Systems with Polynomial-Form Faults. Intelligence & Control 2026, 2 (3), 6. https://doi.org/10.53941/ic.2026.100013.
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