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Volume 7 Issue 5
Sep.  2020

IEEE/CAA Journal of Automatica Sinica

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Article Navigation >  IEEE/CAA Journal of Automatica Sinica  > 2020  >  7(5): 1215-1224
Ning Wang and Xiaojian Li, "Secure Synchronization Control for a Class ofCyber-Physical Systems WithUnknown Dynamics," IEEE/CAA J. Autom. Sinica, vol. 7, no. 5, pp. 1215-1224, Sept. 2020. doi: 10.1109/JAS.2020.1003192
Citation: Ning Wang and Xiaojian Li, "Secure Synchronization Control for a Class ofCyber-Physical Systems WithUnknown Dynamics," IEEE/CAA J. Autom. Sinica, vol. 7, no. 5, pp. 1215-1224, Sept. 2020. doi: 10.1109/JAS.2020.1003192
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Secure Synchronization Control for a Class of Cyber-Physical Systems With Unknown Dynamics

doi: 10.1109/JAS.2020.1003192
Funds:  This work was supported in part by the National Natural Science Foundation of China (61873050), the Fundamental Research Funds for the Central Universities (N180405022, N2004010), the Research Fund of State Key Laboratory of Synthetical Automation for Process Industries (2018ZCX14), and Liaoning Revitalization Talents Program (XLYC1907088)
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    Abstract
  • This paper investigates the secure synchronization control problem for a class of cyber-physical systems (CPSs) with unknown system matrices and intermittent denial-of-service (DoS) attacks. For the attack free case, an optimal control law consisting of a feedback control and a compensated feedforward control is proposed to achieve the synchronization, and the feedback control gain matrix is learned by iteratively solving an algebraic Riccati equation (ARE). For considering the attack cases, it is difficult to perform the stability analysis of the synchronization errors by using the existing Lyapunov function method due to the presence of unknown system matrices. In order to overcome this difficulty, a matrix polynomial replacement method is given and it is shown that, the proposed optimal control law can still guarantee the asymptotical convergence of synchronization errors if two inequality conditions related with the DoS attacks hold. Finally, two examples are given to illustrate the effectiveness of the proposed approaches.

     

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  • [1]
    V. Reppa, M. M. Polycarpou, and C. G. Panayiotou, “Distributed sensor fault diagnosis for a network of interconnected cyberphysical systems,” IEEE Trans. Control of Network Systems, vol. 2, no. 1, pp. 11–23, 2015. doi: 10.1109/TCNS.2014.2367362
    [2]
    A. Bidram, A. Davoudi, F. L. Lewis, and J. M. Guerrero, “Distributed cooperative secondary control of microgrids using feedback linearization,” IEEE Trans. Power Systems, vol. 28, no. 3, pp. 3462–3470, 2013. doi: 10.1109/TPWRS.2013.2247071
    [3]
    A. Isidori, L. Marconi, and G. Casadei, “Robust output synchronization of a network of heterogeneous nonlinear agents via nonlinear regulation theory,” IEEE Trans. Autom. Control, vol. 59, no. 10, pp. 2680–2691, 2014. doi: 10.1109/TAC.2014.2326213
    [4]
    M. H. Zhu and S. Martinez, “On the performance analysis of resilient networked control systems under replay attacks,” IEEE Trans. Autom. Control, vol. 59, no. 3, pp. 804–808, 2014. doi: 10.1109/TAC.2013.2279896
    [5]
    H. Liu, X. H. Cao, J. P. He, P. Cheng, C. G. Li, J. M. Chen, and Y. X. Sun, “Distributed identification of the most critical node for average consensus,” IEEE Trans. Signal Processing, vol. 63, no. 16, pp. 4315–4328, 2015. doi: 10.1109/TSP.2015.2441039
    [6]
    M. S. Rahman, M. A. Mahmud, A. M. T. Oo, and H. R. Pota, “Multi-agent approach for enhancing security of protection schemes in cyber-physical energy systems,” IEEE Trans. Industrial Informatics, vol. 13, no. 2, pp. 436–447, 2017. doi: 10.1109/TII.2016.2612645
    [7]
    X. Z. Jin, G. H. Yang, and W. W. Che, “Adaptive synchronization of master-slave large-scale systems against bias actuators and network attenuations,” Int. J. Control,Autom. and Systems, vol. 10, no. 6, pp. 1102–1110, 2012. doi: 10.1007/s12555-012-0604-1
    [8]
    Y. Wang, J. L. Xiong, and D. W. C. Ho, “Decentralized control scheme for large-scale systems defined over a graph in presence of communication delays and random missing measurements,” Automatica, vol. 98, pp. 190–200, 2018. doi: 10.1016/j.automatica.2018.09.023
    [9]
    A. Y. Lu and G. H. Yang, “Secure state estimation for cyber-physical systems under sparse sensor attacks via a switched Luenberger observer,” Information Sciences, vol. 417, pp. 454–464, 2017. doi: 10.1016/j.ins.2017.07.029
    [10]
    Y. Shoukry and P. Tabuada, “Event-triggered state observers for sparse sensor noise/attacks,” IEEE Trans. Autom. Control, vol. 61, no. 8, pp. 2079–2091, 2016. doi: 10.1109/TAC.2015.2492159
    [11]
    D. R. Ding, Z. D. Wang, D. W. C. Ho, and G. L. Wei, “Distributed recursive filtering for stochastic systems under uniform quantizations and deception attacks through sensor networks,” Automatica, vol. 78, pp. 231–240, 2017. doi: 10.1016/j.automatica.2016.12.026
    [12]
    X. Huang and J. X. Dong, “Reliable control policy of cyber-physical systems against a class of frequency-constrained sensor and actuator attacks,” IEEE Trans. Cybernetics, vol. 48, no. 12, pp. 3432–3439, 2018. doi: 10.1109/TCYB.2018.2815758
    [13]
    G. Franzè, F. Tedesco, and W. Lucia, “Resilient control for cyberPhysical systems subject to replay attacks,” IEEE Control Systems Letters, vol. 3, no. 4, pp. 984–989, 2019. doi: 10.1109/LCSYS.2019.2920507
    [14]
    C. D. Persis and P. Tesi, “Input-to-state stabilizing control under denial-ofservice,” IEEE Trans. Autom. Control, vol. 60, no. 11, pp. 2930–2944, 2015. doi: 10.1109/TAC.2015.2416924
    [15]
    S. Feng and P. Tesi, “Resilient control under denial-of-service: Robust design,” Automatica, vol. 79, pp. 42–51, 2017. doi: 10.1016/j.automatica.2017.01.031
    [16]
    V. S. Dolk, P. Tesi, C. De Persis, and W. P. M. H. Heemels, “Eventtriggered control systems under denial-of-service attacks,” IEEE Trans. Control of Network Systems, vol. 4, no. 1, pp. 93–105, 2017. doi: 10.1109/TCNS.2016.2613445
    [17]
    A. Y. Lu and G. H. Yang, “Input-to-state stabilizing control for cyberphysical systems with multiple transmission channels under denial of service,” IEEE Trans. Autom. Control, vol. 63, no. 6, pp. 1813–1820, 2018. doi: 10.1109/TAC.2017.2751999
    [18]
    S. L. Hu, D. Yue, X. L. Chen, Z. H. Cheng, and X. P. Xie, “Resilient H∞ filtering for event-triggered networked systems under nonperiodic DoS jamming attacks,” IEEE Trans. Systems, Man, and Cybernetics: Systems, Mar. 2019.
    [19]
    L. W. An and G. H. Yang, “Decentralized adaptive fuzzy secure control for nonlinear uncertain interconnected systems against intermittent DoS attacks,” IEEE Trans. Cybernetics, vol. 49, no. 3, pp. 827–838, 2017.
    [20]
    L. H. Peng, L. Shi, X. H. Cao, and C. Y. Sun, “Optimal attack energy allocation against remote state estimation,” IEEE Trans. Autom. Control, vol. 63, no. 7, pp. 2199–2205, 2017.
    [21]
    K. M. Ding, Y. Z. Li, D. E. Quevedo, S. Dey, and L. Shi, “A multi-channel transmission schedule for remote state estimation under DoS attacks,” Automatica, vol. 78, pp. 194–201, 2017. doi: 10.1016/j.automatica.2016.12.020
    [22]
    B. Chen, D. W. C. Ho, W. A. Zhang, and L. Yu, “Distributed dimensionality reduction fusion estimation for cyber-physical systems under DoS attacks,” IEEE Trans. Systems,Man,and Cybernetics:Systems, vol. 49, no. 2, pp. 455–468, 2017.
    [23]
    D. Zhang, L. Liu, and G. Feng, “Consensus of heterogeneous linear multiagent systems subject to aperiodic sampled-data and DoS attack,” IEEE Trans. Cybernetics, vol. 49, no. 4, pp. 1501–1511, 2018.
    [24]
    Y. W. Wang, H. O. Wang, J. W. Xiao, and Z. H. Guan, “Synchronization of complex dynamical networks under recoverable attacks,” Automatica, vol. 46, no. 1, pp. 197–203, 2010. doi: 10.1016/j.automatica.2009.10.024
    [25]
    Y. Xu, M. Fang, P. Shi, and Z. G. Wu, “Event-based secure consensus of mutiagent systems against DoS attacks,” IEEE Trans. Cybernetics, Jun. 2019.
    [26]
    Z. Feng and G. Q. Hu, “Secure cooperative event-triggered control of linear multiagent systems under DoS attacks,” IEEE Trans. Control Systems Technology, 2019.
    [27]
    B. Kiumarsi and F. L. Lewis, “Output synchronization of heterogeneous discrete-time systems: A model-free optimal approach,” Automatica, vol. 84, pp. 86–94, 2017. doi: 10.1016/j.automatica.2017.07.004
    [28]
    H. Modares, S. P. Nageshrao, G. A. D. Lopes, R. Babuška, and F. L. Lewis, “Optimal model-free output synchronization of heterogeneous systems using off-policy reinforcement learning,” Automatica, vol. 71, pp. 334–341, 2016. doi: 10.1016/j.automatica.2016.05.017
    [29]
    N. Wang and X. J. Li, “Optimal output synchronization control of a class of complex dynamical networks with partially unknown system dynamics,” IEEE Trans. Systems, Man, and Cybernetics: Systems, 2018.
    [30]
    Y. W. Cao, G. H. Yang, and X. J. Li, “Optimal synchronization controller design for complex dynamical networks with unknown system dynamics,” J. Franklin Institute, vol. 356, no. 12, pp. 6071–6086, 2019. doi: 10.1016/j.jfranklin.2018.11.054
    [31]
    M. Y. Li and Z. S. Shuai, “Global-stability problem for coupled systems of differential equations on networks,” J. Differential Equations, vol. 248, no. 1, pp. 1–20, 2010. doi: 10.1016/j.jde.2009.09.003
    [32]
    H. Zhang, P. Cheng, L. Shi, and J. M. Chen, “Optimal denial-of-service attack scheduling with energy constraint,” IEEE Trans. Autom. Control, vol. 60, no. 11, pp. 3023–3028, 2015. doi: 10.1109/TAC.2015.2409905
    [33]
    H. Zhang, P. Cheng, L. Shi, and J. M. Chen, “Optimal DoS attack scheduling in wireless networked control system,” IEEE Trans. Control Systems Technology, vol. 24, no. 3, pp. 834–852, 2016.
    [34]
    F. L. Lewis, D. Vrabie, and V. Syrmos, Optimal Control, 3rd edition, New York: Wiley, 2012.
    [35]
    D. Kleinman, “On an iterative technique for Riccati equation computations,” IEEE Trans. Autom. Control, vol. 13, no. 1, pp. 114–115, 1968. doi: 10.1109/TAC.1968.1098829
    [36]
    Y. Jiang and Z. P. Jiang, “Computational adaptive optimal control for continuous-time linear systems with completely unknown dynamics,” Automatica, vol. 48, pp. 2699–2704, 2012. doi: 10.1016/j.automatica.2012.06.096
    [37]
    H. K. Khalil, Nonlinear Systems, 3rd edition, Prentice Hall, Englewood Cliffs, NJ, 2003.
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    Highlights

    • This paper investigates the secure synchronization control problem for a class of cyber-physical systems (CPSs) with unknown system matrices and intermittent denial-of-service (DoS) attacks.
    • For the attack free case, an optimal control law consisting of a feedback control and a compensated feedforward control is proposed to achieve the synchronization, and the feedback control gain matrix is learned by iteratively solving an Algebraic Riccati Equation (ARE).
    • Considering the attack cases, it is difficult to perform the stability analysis of the synchronization errors by using the existing Lyapunov function methods due to the presence of unknown system matrices. In order to overcome this difficulty, a matrix polynomial replacement method is given and it is proved that, the proposed optimal control law can still guarantee the convergence of synchronization errors if two inequality conditions related with the DoS attacks hold.

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