A journal of IEEE and CAA , publishes high-quality papers in English on original theoretical/experimental research and development in all areas of automation
Volume 7 Issue 5
Sep.  2020

IEEE/CAA Journal of Automatica Sinica

  • JCR Impact Factor: 6.171, Top 11% (SCI Q1)
    CiteScore: 11.2, Top 5% (Q1)
    Google Scholar h5-index: 51, TOP 8
Turn off MathJax
Article Contents
Wangli He, Zekun Mo, Qing-Long Han and Feng Qian, "Secure Impulsive Synchronization in Lipschitz-Type Multi-Agent Systems Subject to Deception Attacks," IEEE/CAA J. Autom. Sinica, vol. 7, no. 5, pp. 1326-1334, Sept. 2020. doi: 10.1109/JAS.2020.1003297
Citation: Wangli He, Zekun Mo, Qing-Long Han and Feng Qian, "Secure Impulsive Synchronization in Lipschitz-Type Multi-Agent Systems Subject to Deception Attacks," IEEE/CAA J. Autom. Sinica, vol. 7, no. 5, pp. 1326-1334, Sept. 2020. doi: 10.1109/JAS.2020.1003297

Secure Impulsive Synchronization in Lipschitz-Type Multi-Agent Systems Subject to Deception Attacks

doi: 10.1109/JAS.2020.1003297
Funds:  This work was supported by the National Natural Science Foundation of China (61988101, 61922030, 61773163), Shanghai Rising-Star Program (18QA1401400), the International (Regional) Cooperation and Exchange Project (61720106008), the Natural Science Foundation of Shanghai (17ZR1406800), the Fundamental Research Funds for the Central Universities, and the 111 Project (B17017)
More Information
  • Cyber attacks pose severe threats on synchronization of multi-agent systems. Deception attack, as a typical type of cyber attack, can bypass the surveillance of the attack detection mechanism silently, resulting in a heavy loss. Therefore, the problem of mean-square bounded synchronization in multi-agent systems subject to deception attacks is investigated in this paper. The control signals can be replaced with false data from controller-to-actuator channels or the controller. The success of the attack is measured through a stochastic variable. A distributed impulsive controller using a pinning strategy is redesigned, which ensures that mean-square bounded synchronization is achieved in the presence of deception attacks. Some sufficient conditions are derived, in which upper bounds of the synchronization error are given. Finally, two numerical simulations with symmetric and asymmetric network topologies are given to illustrate the theoretical results.


  • loading
  • [1]
    X.-M. Zhang, Q.-L. Han, X. H. Ge, D. R. Ding, L. Ding, D. Yue, and C. Peng, “Networked control systems: A survey of trends and techniques,” IEEE/CAA J. Autom. Sinica, vol. 7, no. 1, pp. 1–17, Jan. 2020. doi: 10.1109/JAS.2019.1911861
    W. He, T. Luo, Y. Tang, W. Du, Y.-C. Tian, and F. Qian, “Secure communication based on quantized synchronization of chaotic neural networks under an event-triggered strategy,” IEEE Trans. Neural Netw. Learn. Syst., Oct. 2019.
    Y. C. Cao, W. W. Yu, W. Ren, and G. Chen, “An overview of recent progress in the study of distributed multi-agent coordination,” IEEE Trans. Ind. Inform., vol. 9, no. 1, pp. 427–438, Feb. 2013. doi: 10.1109/TII.2012.2219061
    G. H. Wen, W. W. Yu, X. H. Yu, and J. H. Lv, “Complex cyber-physical networks: From cybersecurity to security control,” J. Syst. Sci. Complex., vol. 30, no. 1, pp. 46–67, Feb. 2017. doi: 10.1007/s11424-017-6181-x
    W. L. He, B. Xu, Q.-L. Han, and F. Qian “Adaptive consensus control of linear multiagent systems with dynamic event-triggered strategies,” IEEE Trans. Cybern., Jun. 2019.
    E. M. Amullen, S. Shetty, and L. H. Keel, “Secured formation control for multi-agent systems under DoS attacks,” in Proc. IEEE Symp. Technol. Homel. Secur., HST, pp. 1–6, Sep. 2016.
    G. H. Wen, Z. S. Duan, G. R. Chen, and W. W. Yu, “Consensus tracking of multi-agent systems with Lipschitz-type node dynamics and switching topologies,” IEEE Trans. Circuits Syst. I-Regul. Pap., vol. 61, no. 2, pp. 499–511, Feb. 2014. doi: 10.1109/TCSI.2013.2268091
    Z. Zhu, Y. Pan, Q. Zhou, and C. Lu, “Event-triggered adaptive fuzzy control for stochastic nonlinear systems with unmeasured states and unknown backlash-like hysteresis,”IEEE Trans. Fuzzy Syst., Feb. 2020.
    A. Bonci, M. Pirani, and S. Longhi, “Tiny cyber-physical systems for performance improvement in the factory of the future,” IEEE Trans. Ind. Inform., vol. 15, no. 3, pp. 1598–1608, Jul. 2018.
    H. Sandberg, S. Amin, and K. H. Johansson, “Cyber-physical security in networked control systems: An introduction to the issue,” IEEE Control Syst. Mag., vol. 35, no. 1, pp. 20–23, Feb. 2015. doi: 10.1109/MCS.2014.2364708
    Y. Mo, H. J. Kim, K. Brancik, D. Dickinson, H. Lee, A. Perring, and B. Sinopoli, “Cyber-physical security of a smart grid infrastructure,” Proc. IEEE, vol. 100, no. 1, pp. 195–209, Jan. 2012. doi: 10.1109/JPROC.2011.2161428
    A. A. Cardenas, S. Amin, and S. Sastry, “Secure control: Towards survivable cyber-physical systems,” in Proc. Int. Conf. Distrib. Comput. Syst., pp. 495–500, Jun. 2008.
    H. T. Sun, C. Peng, T. C. Yang, H. Zhang, and W. L. He, “Resilient control of networked control systems with stochastic denial of service attacks,” Neurocomputing, vol. 270, pp. 170–177, Dec. 2017. doi: 10.1016/j.neucom.2017.02.093
    A. Y. Lu and G. H. Yang, “Distributed consensus control for multiagent systems under denial-of-service,” Inf. Sci., vol. 439, pp. 95–107, May 2018.
    D. Zhang, L. Liu, and G. Feng, “Consensus of heterogeneous linear multiagent systems subject to a periodic sampled-data and DoS attack,” IEEE Trans. Cybern., vol. 49, no. 4, pp. 1501–1511, Apr. 2019. doi: 10.1109/TCYB.2018.2806387
    Z. Feng, G. H. Wen, and G. Q. Hu, “Distributed secure coordinated control for multiagent systems under strategic attacks,” IEEE Trans. Cybern., vol. 47, no. 5, pp. 1273–1284, May 2017. doi: 10.1109/TCYB.2016.2544062
    Z. Feng, G. Q. Hu, and G. H. Wen, “Distributed consensus tracking for multi-agent systems under two types of attacks,” Int. J. Robust Nonlinear Control, vol. 26, no. 5, pp. 896–918, Mar. 2016. doi: 10.1002/rnc.3342
    Y. Wan, J. D. Cao, G. R. Chen, and W. Huang, “Distributed observerbased cyber-security control of complex dynamical networks,” IEEE Trans. Circuits Syst. I-Regul. Pap., vol. 64, no. 11, pp. 2966–2975, Nov. 2017. doi: 10.1109/TCSI.2017.2708113
    Z. S. Wang, J. Sun, and H. G. Zhang, “Stability analysis of T-S fuzzy control system with sampled-dropouts based on time-varying Lyapunov function method,”IEEE Trans. Syst., Man, Cybern., Syst., Apr. 2018.
    J. Sun and Z. S. Wang, “Consensus of multi-agent systems with intermittent communications via sampling time unit approach,” Neurocomputing, vol. 397, pp. 149–159, Jul. 2020. doi: 10.1016/j.neucom.2020.02.055
    S. Wu, Z. Y. Guo, D. W. Shi, K. H. Johansson, and L. Shi, “Optimal innovation-based deception attack on remote state estimation,” in Proc. American Control Conf., pp. 3017–3022, Jul. 2017.
    Y. Liu, P. Ning, and M. K. Reiter, “False data injection attacks against state estimation in electric power grids,” ACM Trans. Inf. Syst. Secur., vol. 14, no. 1, pp. 21–32, Jun. 2011.
    R. L. Deng and H. Liang, “False data injection attacks with limited susceptance information and new countermeasures in smart grid,” IEEE Trans. Ind. Inform., vol. 15, no. 3, pp. 1619–1628, Mar. 2019. doi: 10.1109/TII.2018.2863256
    A. J. Kerns, D. P. Shepard, J. A. Bhatti, and T. E. Humphreys, “Unmanned aircraft capture and control via GPS spoofing,” J. Field Robot., vol. 31, no. 4, pp. 617–636, Aug. 2014. doi: 10.1002/rob.21513
    H. Li, Y. Wu, and M. Chen, “Adaptive fault-tolerant tracking control for discrete-time multiagent systems via reinforcement learning algorithm,” IEEE Trans. Cybern., May. 2020.
    Z. Y. Guo, D. W. Shi, K. H. Johansson, and L. Shi, “Optimal linear cyber-attack on remote state estimation,” IEEE Trans. Control Netw. Syst., vol. 4, no. 1, pp. 4–13, Mar. 2017. doi: 10.1109/TCNS.2016.2570003
    Y. Z. Li, L. Shi, and T. W. Chen, “Detection against linear deception attacks on multi-sensor remote state estimation,” IEEE Trans. Control Netw. Syst., vol. 5, no. 3, pp. 846–856, Sep. 2018. doi: 10.1109/TCNS.2017.2648508
    L. F. Ma, Z. D. Wang, and Y. Yuan, “Consensus control for nonlinear multi-agent systems subject to deception attacks,” in Proc. Int. Conf. Autom. Comput., ICAC, pp. 21–26, Oct. 2016.
    A. Mustafa and H. Modares, “Attack analysis and resilient control design for discrete-time distributed multi-agent systems,” IEEE Robot. Autom. Lett., vol. 5, no. 2, pp. 369–376, Apr. 2020. doi: 10.1109/LRA.2019.2959726
    J. Q. Lu, J. Kurths, J. D. Cao, N. Mahdavi, and C. Huang, “Synchronization control for nonlinear stochastic dynamical networks: Pinning impulsive strategy,” IEEE Trans. Neural Netw. Learn. Syst., vol. 23, no. 2, pp. 285–292, Feb. 2012. doi: 10.1109/TNNLS.2011.2179312
    N. Mahdavi, M. B. Menhaj, J. Kurths, J. Q. Lu, and A. Afshar, “Pinning impulsive synchronization of complex dynamical networks,” Int. J. Bifurcation Chaos, vol. 22, no. 10, pp. 1250239, Oct. 2012.
    W. L. He, F. Qian, J. Lam, G. R. Chen, Q.-L. Han, and J. Kurths, “Quasi-synchronization of heterogeneous dynamic networks via distributed impulsive control,” Automatica, vol. 62, pp. 249–262, Dec. 2015. doi: 10.1016/j.automatica.2015.09.028
    W. L. He, G. R. Chen, Q.-L. Han, and F. Qian, “Network-based leader-following consensus of nonlinear multi-agent systems via distributed impulsive control,” Inf. Sci., vol. 380, pp. 145–158, Feb. 2017. doi: 10.1016/j.ins.2015.06.005
    W. L. He, F. Qian, Q.-L. Han, and G. R. Chen, “Almost sure stability of nonlinear systems under random and impulsive sequential attacks,” IEEE Trans. Autom. Control, Feb. 2020.
    X. S. Yang, X. D. Li, J. Q. Lu, and Z. S. Cheng, “Synchronization of time-delayed complex networks with switching topology via hybrid actuator fault and impulsive effects control,” IEEE Trans. Cybern., Sep. 2019.
    W. L. He, X. Y. Gao, W. M. Zhong, and F. Qian, “Secure impulsive synchronization control of multi-agent systems under deception attacks,” Inf. Sci., vol. 459, pp. 354–368, Aug. 2018. doi: 10.1016/j.ins.2018.04.020


    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


    Article Metrics

    Article views (1230) PDF downloads(98) Cited by()


    • A mathematical model of leader-following MASs with distributed impulsive control suffered from replacement deception attacks is built, in which the data integrity is destroyed by the occasional replacement of the control signal with the injected bad data. A stochastic variable following Bernoulli distribution is introduced to describe if the attack is successful.
    • Sufficient conditions for mean-square bounded synchronization are derived in which the nonzero upper bound of the error is given. The attack intensity and the attack tolerance probability related to the design of the impulsive interval, coupling strength are discussed.
    • For symmetric networks, how to choose the coupling strength, the impulsive interval and the attack intensity and probability the systems can render are also given.
    • 1335
    • An asymmetric barrier Lyapunov functions (ABLFs) based neural control is proposed.
    • All states are guaranteed to stay in the pre-given time-varying ranges within finite time.
    • The system output is driven to track the desired signal as quickly as possible.


    DownLoad:  Full-Size Img  PowerPoint