Research article

Adaptive tracking control for multi-agent systems with deception attacks and DoS attacks

  • Published: 02 March 2026
  • This paper investigates the adaptive tracking control problem for a class of strict-feedback uncertain nonlinear multi-agent systems (MASs) with input dead zones under the simultaneous presence of deception attacks and denial-of-service (DoS) attacks. A distributed leader-state estimator based on sampled data with time delays is proposed to estimate the leader's state under DoS attacks. To overcome the nonexistence of higher-order derivatives of the leader's estimated state, a filter is proposed and implemented. Based on the attacked output signals, a fuzzy state observer is constructed to reconstruct the unmeasurable states of the system. A fuzzy adaptive dead-zone control scheme is designed based on the backstepping method to mitigate the adverse effects of DoS attacks, deception attacks, dead zones, and uncertain nonlinear dynamics, while enabling the system to achieve the tracking control objective. Through Lyapunov stability analysis, the proposed control scheme is proven to guarantee that all signals in the closed-loop system are bounded and the tracking error converges to a neighborhood around the origin. Finally, simulations are conducted to verify the effectiveness of the theoretical results.

    Citation: Wan Yu, Hui Yu. Adaptive tracking control for multi-agent systems with deception attacks and DoS attacks[J]. Electronic Research Archive, 2026, 34(3): 1857-1884. doi: 10.3934/era.2026083

    Related Papers:

  • This paper investigates the adaptive tracking control problem for a class of strict-feedback uncertain nonlinear multi-agent systems (MASs) with input dead zones under the simultaneous presence of deception attacks and denial-of-service (DoS) attacks. A distributed leader-state estimator based on sampled data with time delays is proposed to estimate the leader's state under DoS attacks. To overcome the nonexistence of higher-order derivatives of the leader's estimated state, a filter is proposed and implemented. Based on the attacked output signals, a fuzzy state observer is constructed to reconstruct the unmeasurable states of the system. A fuzzy adaptive dead-zone control scheme is designed based on the backstepping method to mitigate the adverse effects of DoS attacks, deception attacks, dead zones, and uncertain nonlinear dynamics, while enabling the system to achieve the tracking control objective. Through Lyapunov stability analysis, the proposed control scheme is proven to guarantee that all signals in the closed-loop system are bounded and the tracking error converges to a neighborhood around the origin. Finally, simulations are conducted to verify the effectiveness of the theoretical results.



    加载中


    [1] P. Leitão, S. Karnouskos, L. Ribeiro, J. Lee, T. Strasser, A. W. Colombo, Smart agents in industrial cyber-physical systems, Proceedings of the IEEE, 104 (2016), 1086–1101. https://doi.org/10.1109/JPROC.2016.2521931 doi: 10.1109/JPROC.2016.2521931
    [2] D. Zhang, G. Feng, Y. Shi, D. Srinivasan, Physical safety and cyber security analysis of multi-agent systems: A survey of recent advances, IEEE/CAA J. Autom. Sin., 8 (2021), 319–333. https://doi.org/10.1109/JAS.2021.1003820 doi: 10.1109/JAS.2021.1003820
    [3] K. Zhang, Y. Shi, S. Karnouskos, T. Sauter, H. Fang, A. W. Colombo, Advancements in industrial cyber-physical systems: An overview and perspectives, IEEE Trans. Ind. Inf., 19 (2023), 716–729. https://doi.org/10.1109/TII.2022.3199481 doi: 10.1109/TII.2022.3199481
    [4] G. E. M. Abro, Z. A. Ali, R. J. Masood, Synergistic UAV motion: A comprehensive review on advancing multi-agent coordination, ICCK Trans. Sens. Commun. Control, 1 (2024), 72–88. https://doi.org/10.62762/TSCC.2024.211408 doi: 10.62762/TSCC.2024.211408
    [5] Z. Qian, W. Lyu, Y. Dai, J. Xu, A consensus-based model predictive control with optimized line-of-sight guidance for formation trajectory tracking of autonomous underwater vehicles, J. Intell. Robotic Syst., 106 (2022), 15. https://doi.org/10.1007/s10846-022-01710-4 doi: 10.1007/s10846-022-01710-4
    [6] H. Çeker, J. Zhuang, S. Upadhyaya, Q. D. La, B. Soong, Deception-based game theoretical approach to mitigate DoS attacks, in Decision and Game Theory for Security, (2016), 18–38. https://doi.org/10.1007/978-3-319-47413-7_2
    [7] A. Kazemy, J. Lam, X. Zhang, Event-triggered output feedback synchronization of master–slave neural networks under deception attacks, IEEE Trans. Neural Networks Learn. Syst., 33 (2022), 952–961. https://doi.org/10.1109/TNNLS.2020.3030638 doi: 10.1109/TNNLS.2020.3030638
    [8] N. Zhao, Y. Tian, H. Zhang, E. Herrera-Viedma, Learning-based adaptive fuzzy output feedback control for MIMO nonlinear systems with deception attacks and input saturation, IEEE Trans. Fuzzy Syst., 32 (2024), 2850–2862. https://doi.org/10.1109/TFUZZ.2024.3363839 doi: 10.1109/TFUZZ.2024.3363839
    [9] C. Deng, D. Yue, W. Che, X. Xie, Cooperative fault-tolerant control for a class of nonlinear MASs by resilient learning approach, IEEE Trans. Neural Networks Learn. Syst., 35 (2024), 670–679. https://doi.org/10.1109/TNNLS.2022.3176392 doi: 10.1109/TNNLS.2022.3176392
    [10] M. Zhao, J. Xi, L. Wang, C. Wang, Y. Zheng, Fully distributed secure tracking for leader-following nonlinear multiagent systems with multi-link sequence scaling attacks, IEEE Control Syst. Lett., 8 (2024), 327–332. https://doi.org/10.1109/LCSYS.2024.3375156 doi: 10.1109/LCSYS.2024.3375156
    [11] F. Cheng, H. Liang, H. Wang, G. Zong, N. Xu, Adaptive neural self-triggered bipartite fault-tolerant control for nonlinear MASs with dead-zone constraints, IEEE Trans. Autom. Sci. Eng., 20 (2023), 1663–1674. https://doi.org/10.1109/TASE.2022.3184022 doi: 10.1109/TASE.2022.3184022
    [12] Y. Li, G. Yang, Adaptive fuzzy decentralized control for a class of large-scale nonlinear systems with actuator faults and unknown dead zones, IEEE Trans. Syst. Man Cybern. Syst., 47 (2017), 729–740. https://doi.org/10.1109/TSMC.2016.2521824 doi: 10.1109/TSMC.2016.2521824
    [13] X. Jia, S. Xu, Z. Zhang, G. Cui, Output feedback robust stabilisation for uncertain non-linear systems with dead-zone input, IET Control Theory Appl., 14 (2020), 1828–1836. https://doi.org/10.1049/iet-cta.2020.0107 doi: 10.1049/iet-cta.2020.0107
    [14] Z. Ma, S. Tong, Nonlinear filters-based adaptive fuzzy control of strict-feedback nonlinear systems with unknown asymmetric dead-zone output, IEEE Trans. Autom. Sci. Eng., 21 (2024), 5099–5109. https://doi.org/10.1109/TASE.2023.3308528 doi: 10.1109/TASE.2023.3308528
    [15] R. Shahnazi, Cooperative neuro adaptive control of leader following uncertain multi-agent systems with unknown hysteresis and dead-zone, J. Syst. Sci. Complex, 33 (2020), 312–332. https://doi.org/10.1007/s11424-020-8198-9 doi: 10.1007/s11424-020-8198-9
    [16] D. Liu, Z. Liu, C. L. P. Chen, Y. Zhang, Distributed adaptive neural control for uncertain multi-agent systems with unknown actuator failures and unknown dead zones, Nonlinear Dyn., 99 (2020), 1001–1017. https://doi.org/10.1007/s11071-019-05321-x doi: 10.1007/s11071-019-05321-x
    [17] X. Hou, H. Wu, J. Cao, Practical finite-time synchronization for Lur'e systems with performance constraint and actuator faults: A memory-based quantized dynamic event-triggered control strategy, Appl. Math. Comput., 487 (2025), 129108. https://doi.org/10.1016/j.amc.2024.129108 doi: 10.1016/j.amc.2024.129108
    [18] H. Zeng, Z. Zhu, T. Peng, W. Wang, X. Zhang, Robust tracking control design for a class of nonlinear networked control systems considering bounded package dropouts and external disturbance, IEEE Trans. Fuzzy Syst., 32 (2024), 3608–3617. https://doi.org/10.1109/TFUZZ.2024.3377799 doi: 10.1109/TFUZZ.2024.3377799
    [19] D. Chwa, H. Kwon, Nonlinear robust control of unknown robot manipulator systems with actuators and disturbances using system identification and integral sliding mode disturbance observer, IEEE Access, 10 (2022), 35410–35421. https://doi.org/10.1109/ACCESS.2022.3163306 doi: 10.1109/ACCESS.2022.3163306
    [20] X. Zhao, H. Wu, J. Cao, L. Wang, Prescribed-time synchronization for complex dynamic networks of piecewise smooth systems: a hybrid event-triggering control approach, Qual. Theory Dyn. Syst., 24 (2025), 11. https://doi.org/10.1007/s12346-024-01166-x doi: 10.1007/s12346-024-01166-x
    [21] Y. Jiang, C. Yang, H. Ma, A review of fuzzy logic and neural network based intelligent control design for discrete-time systems, Discrete Dyn. Nat. Soc., 2016 (2016), 7217364. https://doi.org/10.1155/2016/7217364 doi: 10.1155/2016/7217364
    [22] K. Gurney, An Introduction to Neural Networks, $1^{st}$ edition, CRC Press, 1997. https://doi.org/10.1201/9781315273570
    [23] P. Cheng, S. He, G. Xiao, W. Zhang, ESO-based control for 2-D networked control systems with Markov-type DoS attacks, IEEE Trans. Network Sci. Eng., 12 (2025), 4452–4461. https://doi.org/10.1109/TNSE.2025.3572098 doi: 10.1109/TNSE.2025.3572098
    [24] Y. Liu, C. Deng, X. Xie, W. Che, L. Zhang, S. Fan, Cooperative observer-based fuzzy tracking control for nonlinear MASs under DoS attacks, IEEE Trans. Fuzzy Syst., 32 (2024), 767–777. https://doi.org/10.1109/TFUZZ.2023.3306372 doi: 10.1109/TFUZZ.2023.3306372
    [25] H. Zhou, S. Tong, Fuzzy adaptive event-triggered resilient formation control for nonlinear multiagent systems under DoS attacks and input saturation, IEEE Trans. Syst. Man Cybern. Syst., 54 (2024), 3665–3674. https://doi.org/10.1109/TSMC.2024.3369093 doi: 10.1109/TSMC.2024.3369093
    [26] C. Deng, M. J. Er, G. Yang, N. Wang, Event-triggered consensus of linear multiagent systems with time-varying communication delays, IEEE Trans. Cybern., 50 (2020), 2916–2925. https://doi.org/10.1109/TCYB.2019.2922740 doi: 10.1109/TCYB.2019.2922740
    [27] H. Zhou, S. Tong, Fuzzy adaptive resilient formation control for nonlinear multiagent systems subject to DoS attacks, IEEE Trans. Fuzzy Syst., 32 (2024), 1446–1454. https://doi.org/10.1109/TFUZZ.2023.3327140 doi: 10.1109/TFUZZ.2023.3327140
    [28] Y. Wang, Y. Yang, L. Wu, Adaptive consensus control of multi-agent systems with dead-zone input, in 2024 14th Asian Control Conference (ASCC), (2024), 1103–1108.
    [29] H. Wu, X. Zhao, L. Wang, J. Cao, Observer-based fixed-time topology identification and synchronization for complex networks via quantized pinning control strategy, Appl. Math. Comput., 507 (2025), 129568. https://doi.org/10.1016/j.amc.2025.129568 doi: 10.1016/j.amc.2025.129568
    [30] C. Deng, C. Wen, MAS-based distributed resilient control for a class of cyber-physical systems with communication delays under DoS attacks, IEEE Trans. Cybern., 51 (2021), 2347–2358. https://doi.org/10.1109/TCYB.2020.2972686 doi: 10.1109/TCYB.2020.2972686
    [31] R. Olfati-Saber, J. A. Fax, R. M. Murray, Consensus and cooperation in networked multi-agent systems, in Proceedings of the IEEE, 95 (2007), 215–233. https://doi.org/10.1109/JPROC.2006.887293
    [32] Y. Shang, B. Chen, C. Lin, Consensus tracking control for distributed nonlinear multiagent systems via adaptive neural backstepping approach, IEEE Trans. Syst. Man Cybern. Syst., 50 (2020), 2436–2444. https://doi.org/10.1109/TSMC.2018.2816928 doi: 10.1109/TSMC.2018.2816928
    [33] H. Deng, M. Krstic, Output-feedback stochastic nonlinear stabilization, IEEE Trans. Autom. Control, 44 (1999), 328–333. https://doi.org/10.1109/9.746260 doi: 10.1109/9.746260
    [34] L. Ding, Q. Han, G. Guo, Network-based leader-following consensus for distributed multi-agent systems, Automatica, 49 (2013), 2281–2286. https://doi.org/10.1016/j.automatica.2013.04.021 doi: 10.1016/j.automatica.2013.04.021
    [35] P. Park, J. W. Ko, C. Jeong, Reciprocally convex approach to stability of systems with time-varying delays, Automatica, 47 (2011), 235–238. https://doi.org/10.1016/j.automatica.2010.10.014 doi: 10.1016/j.automatica.2010.10.014
    [36] N. Wang, Y. Wang, J. H. Park, M. Lv, F. Zhang, Fuzzy adaptive finite-time consensus tracking control of high-order nonlinear multi-agent networks with dead zone, Nonlinear Dyn., 106 (2021), 3363–3378. https://doi.org/10.1007/s11071-021-06956-5 doi: 10.1007/s11071-021-06956-5
    [37] Y. Ma, W. Che, C. Deng, Z. Wu, Observer-based event-triggered containment control for MASs under DoS attacks, IEEE Trans. Cybern., 52 (2022), 13156–13167. https://doi.org/10.1109/TCYB.2021.3104178 doi: 10.1109/TCYB.2021.3104178
  • Reader Comments
  • © 2026 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Metrics

Article views(198) PDF downloads(10) Cited by(0)

Article outline

Figures and Tables

Figures(7)

Other Articles By Authors

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return

Catalog