Advertisement

Guaranteed Cost Control of Networked Control Systems with DoS Attack and Time-varying Delay

  • Yeping Shen
  • Wen-an Zhang
  • Hongjie Ni
  • Dan ZhangEmail author
  • Li Yu
Regular Papers Control Theory and Applications
  • 35 Downloads

Abstract

This paper is concerned with the guaranteed cost control for a class of networked control systems (NCSs) with denial-of-service (DoS) attack and time-varying communication delay. First, an uncertain switched system model is proposed that is capable of capturing the DoS attack and the short time-varying delay simultaneously. To achieve the exponential stability with a guaranteed cost performance level, a sufficient condition is derived in terms of matrix inequalities. In our work, the proposed design condition establishes several quantitative relations between system performance and attack parameters. Moreover, the critical value of strong attack frequency is also obtained. Finally, a practical example is given to show the effectiveness of our results.

Keywords

Denial-of-service (DoS) attack guaranteed cost control networked control systems (NCSs) switched system time-varying delay 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. [1]
    P. Ogren, E. Fiorelli, and N. E. Leonard, “Cooperative control of mobile sensor networks: Adaptive gradient climbing in a distributed environment,” IEEE Transactions on Automatic Control, vol. 49, no. 8, pp. 1292–1302, 2004.MathSciNetCrossRefzbMATHGoogle Scholar
  2. [2]
    D. Zhang, Z. H. Xu, D. Srinivasan, and L. Yu, “Leaderfollower consensus of multiagent systems with energy constraints: a Markovian system approach,” IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 47, no. 7, pp. 1727–1736, 2017.CrossRefGoogle Scholar
  3. [3]
    Z. H. Pang, G. P. Liu, D. H. Zhou, and D. H. Sun, “Design and performance analysis of networked predictive control systems based on input-output difference equation model,” International Journal of Control Automation and Systems, vol. 15, no. 1, pp. 416–426, 2017.CrossRefGoogle Scholar
  4. [4]
    D. Zhang, Z. H. Xu, H. R. Karimi, Q. G. Wang, and L. Yu, “Distributed H∞ output-feedback control for consensus of heterogeneous linear multiagent systems with aperiodic sampled-data communications,” IEEE Transactions on Industrial Electronics, vol. 65, no. 6, pp. 4145–4155, 2018.CrossRefGoogle Scholar
  5. [5]
    C. Peng and J. Zhang, “Delay-distribution-dependent load frequency control of power systems with probabilistic interval delays,” IEEE Transactions on Power Systems, vol. 31, no. 4, pp. 3309–3317, 2016.CrossRefGoogle Scholar
  6. [6]
    J. P. Farwell and R. Rohozinski, “Stuxnet and the future of cyber war,” Survival, vol. 53, no. 1, pp. 23–40, 2011.CrossRefGoogle Scholar
  7. [7]
    R. S. Smith, “Covert misappropriation of networked control systems: presenting a feedback structure,” IEEE Control Systems, vol. 35, no. 1, pp. 82–92, 2015.MathSciNetCrossRefGoogle Scholar
  8. [8]
    M. Long, C. H. J. Wu, and J. Y. Hung, “Denial-of-service attacks on network-based control systems: Impact and mitigation,” IEEE Transactions on Industrial Informatics, vol. 1, no. 2, pp. 85–96, 2005.CrossRefGoogle Scholar
  9. [9]
    Y. Yuan, F. C. Sun, and Q. Y. Zhu, “Resilient control in the presence of DoS attack: switched system approach,” International Journal of Control Automation and Systems, vol. 13, no. 9, pp. 1423–1435, 2015.CrossRefGoogle Scholar
  10. [10]
    H. Zhang, P. Cheng, L. Shi, and J. M. Chen, “Optimal denial-of-service attack scheduling with energy constraint,” IEEE Transactions on Automatic Control, vol. 60, no. 11, pp. 3023–3028, 2015.MathSciNetCrossRefzbMATHGoogle Scholar
  11. [11]
    D. Zhang, L. Liu, and G. Feng, “Consensus of heterogeneous linear multiagent systems subject to aperiodic sampled-data and DoS attack,” IEEE Transactions on Cybernetics, 2018. DOI: 10.1109/TCYB.2018.2806387Google Scholar
  12. [12]
    G. K. Befekadu, V. Gup, and P. J. Antsaklis, “Risksensitive control under markov modulated denial-ofservice (DoS) attack strategies,” IEEE Transactions on Automatic Control, vol. 60, no. 12, pp. 3299–3304, 2015.MathSciNetCrossRefzbMATHGoogle Scholar
  13. [13]
    J. L. Liu, E. G. Tian, X. P. Xie, and H. Lin, “Distributed event-triggered control for networked control systems with stochastic cyber-attacks,” Journal of the Franklin Institute, 2018. DOI: 10.1016/j.jfranklin.2018.01.048Google Scholar
  14. [14]
    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, 2017. 0.1016/j.neucom.2017.02.093CrossRefGoogle Scholar
  15. [15]
    H. Zhang and W. X. Zheng, “Denial-of-service power dispatch against linear quadratic control via a fading channel,” IEEE Transactions on Automatic Control, vol. 63, no. 9, pp. 3032–3039, 2018.MathSciNetCrossRefzbMATHGoogle Scholar
  16. [16]
    A. Cetinkaya, H. Ishii, and T. Hayakawa, “Analysis of stochastic switched systems with application to networked control under jamming attacks,” IEEE Transactions on Automatic Control, 2018. DOI: 10.1109/TAC.2018.2832466Google Scholar
  17. [17]
    H. Zhang, P. Cheng, L. Shi, and J. M. Chen, “Optimal DoS attack scheduling in wireless networked control system,” IEEE Transactions Control Systems Technology, vol. 24, no. 3, pp. 843–852, 2016.CrossRefGoogle Scholar
  18. [18]
    A. Gupta, C. Langbort, and T. Basar, “Optimal control in the presence of an intelligent jammer with limited actions,” Proceedings of the IEEE Conference Decision and Control, pp. 1096–1101, 2010.Google Scholar
  19. [19]
    C. D. Persis and P. Tesi, “Networked control of nonlinear systems under denial-of-service,” Systems and Control Letters, vol. 96, no. 8, pp. 124–131, 2016.MathSciNetCrossRefzbMATHGoogle Scholar
  20. [20]
    C. D. Persis and P. Tesi, “Input-to-state stabilizing control under denial of service,” IEEE Transactions on Automatic Control, vol. 60, no. 11, pp. 2930–2944, 2015.MathSciNetCrossRefzbMATHGoogle Scholar
  21. [21]
    S. Feng and P. Tesi, “Resilient control under denial-ofservice: robust design,” Automatica, vol. 79, no. 8, pp. 42–51, 2017.CrossRefzbMATHGoogle Scholar
  22. [22]
    V. S. Dolk, P. Tesi, C. D. Persis, and W. P. M. H. Heemels, “Event-triggered control systems under denial-of-service attacks,” IEEE Transactions on Control Network Systems, vol. 4, no. 1, pp. 93–105, 2017.MathSciNetCrossRefzbMATHGoogle Scholar
  23. [23]
    H. J. Yang, M. Shi, Y. Q. Ia, and P. Zhang, “Security research on wireless networked control systems subject to jamming attacks,” IEEE Transactions on Cybernetics, 2018. DOI: 10.1109/TCYB.2018.2817249Google Scholar
  24. [24]
    Y. Yuan, H. H. Yuan, L. Guo, H. J. Yang, and S. L. Sun, “Resilient control of networked control system under DoS attacks: a unified game approach,” IEEE Transactions on Industrial Informatics, vol. 12, no. 5, pp. 1786–1794, 2016.CrossRefGoogle Scholar
  25. [25]
    G. S. Deaecto, M. Souza, and J. C. Geromel, “Discretetime switched linear systems state feedback design with application to networked control,” IEEE Transactions on Automatic Control, vol. 60, no. 3, pp. 877–881, 2015.MathSciNetCrossRefzbMATHGoogle Scholar
  26. [26]
    M. Wang, J. B. Qiu, M. Chadli, and M. Wang, “A switched system approach to exponential stabilization of sampleddata T-S fuzzy systems with packet dropouts,” IEEE Transactions on Cybernetics, vol. 46, no. 12, pp. 3145–3156, 2016.CrossRefGoogle Scholar
  27. [27]
    D. Zhang, P. Shi, and L. Yu, “Containment control of linear multiagent systems with aperiodic sampling and measurement size reduction,” IEEE Transactions on Neural Networks and Learning Systems, vol. 29, no. 10, pp. 5020–5029, 2018.MathSciNetCrossRefGoogle Scholar
  28. [28]
    D. Zhang, P. Shi, Q. G. Wang, and L. Yu, “Analysis and synthesis of networked control systems: a survey of recent advances and challenges,” ISA Transactions, vol. 66, pp. 376–392, 2017.CrossRefGoogle Scholar
  29. [29]
    J. Cheng, J. H. Park, H. R. Karimi, and H. Shen, “A flexible terminal approach to sampled-data exponentially synchronization of markovian neural networks with time-varying delayed signals,” IEEE Transactions on Cybernetics, vol. 48, no. 8, pp. 2232–2244, 2018.CrossRefGoogle Scholar
  30. [30]
    W. A. Zhang and L. Yu, “New approach to stabilisation of networked control systems with time-varying delays,” IET Control Theory and Applications, vol. 2, no. 12, pp. 1094–1104, 2008.MathSciNetCrossRefGoogle Scholar
  31. [31]
    W. A. Zhang and L. Yu, “BIBO stability and stabilization of networked control systems with short time-varying delays,” International Journal of Robust and Nonlinear Control, vol. 21, no. 8, pp. 295–308, 2011.MathSciNetCrossRefzbMATHGoogle Scholar
  32. [32]
    J. P. Hespanha and A. S. Morse, “Stability of switched systems with average dwell-time,” Proceedings of the IEEE Conference Decision and Control, pp. 2655–2660, 1999.Google Scholar
  33. [33]
    M. Fu and L. Xie, “The sector bound approach to quantized feedback control,” IEEE Transactions on Automatic Control, vol. 50, no. 11, pp. 1698–1711, 2005.MathSciNetCrossRefzbMATHGoogle Scholar
  34. [34]
    L. Ghaoui, F. Oustry, and M. AitRami, “A cone complementarity linearization algorithm for static outputfeedback and related problems,” IEEE Transactions on Automatic Control, vol. 42, no. 8, pp. 1171–1176, 1997.MathSciNetCrossRefzbMATHGoogle Scholar
  35. [35]
    H. B. Li, M. Y. Chow, and Z. S. Sun, “Optimal stabilizing gain selection for networked control systems with time delays and packet losses,” IEEE Transactions on Control Systems Technology, vol. 17, no. 5, pp. 1154–1162, 2009.CrossRefGoogle Scholar
  36. [36]
    S. Bououdeni, M. Chadli, F. Allouani, and S. Filali, “A new approach for fuzzy predictive adaptive controller design using particle swarm optimization algorithm,” International Journal of Innovative Computing, Information and Control, vol. 9, no. 9, pp. 3741–3758, 2013.Google Scholar
  37. [37]
    C. A. Hu, H. Jing, R. R. Wang, F. J. Yan, and M. Chadli, “Robust H¥ output-feedback control for path following of autonomous ground vehicles,” Mechanical Systems and Signal Processing, vol. 70, pp. 414–427, 2016.CrossRefGoogle Scholar
  38. [38]
    T. Youssef, M. Chadli, H. R. Karimi, and R. Wang, “Actuator and sensor faults estimation based on proportional integral observer for TS fuzzy model,” Journal of the Franklin Institute, vol. 354, no. 6, pp. 2524–2542, 2017.MathSciNetCrossRefzbMATHGoogle Scholar
  39. [39]
    J. Cheng, X. H. Chang, J. H. Park, H. Li, and H. L. Wang, “Fuzzy-model-based H∞ control for discrete-time switched systems with quantized feedback and unreliable links,” Information Sciences, vol. 436, pp. 181–196, 2018.MathSciNetCrossRefGoogle Scholar

Copyright information

© ICROS, KIEE and Springer 2019

Authors and Affiliations

  1. 1.Department of AutomationZhejiang University of TechnologyZhejiangP. R. China
  2. 2.Institute of Cyberspace SecurityZhejiang University of TechnologyZhejiangP. R. China

Personalised recommendations