Adaptive Compensation Control under False Data Injection Attack

doi:10.3969/j.issn.1000-1093.2020.11.013

Abstract

Abstract: For the false data injection of cyber physical systems, a designing method of adaptive compensation controller is proposed, which is used for compensation control when the system executor suffers from constant false data injection attack and bounded time-varied false data injection attack. The application of adaptive compensation control algorithm to the false data injection attack shows that the designed adaptive law has the advantages of fast convergence speed and accurate parameter estimation. The compensation control algorithm can substantially compensate for the effects of false data injection attacks. It can be proven that all signals from the final closed-loop system are bounded and the state of the system can asymptotically converge to zero. Finally, the simulation examples are provided to verify the effectiveness of the state feedback adaptive compensation controller proposed in this paper.

Key words: cyberphysicalsystem, falsedatainjectionattack, systemstate, adaptivecompensationcontrol

CLC Number:

TP393.081

LI Xiaohang， ZHANG Zhihu， SONG Chunlin， WU Qiang， DONG Fangyong. Adaptive Compensation Control under False Data Injection Attack[J]. Acta Armamentarii, 2020, 41(11): 2260-2265.

References

［1］王中杰, 谢璐璐. 信息物理融合系统研究综述［J］. 自动化学报, 2011, 37(10): 1157-1166.
WANG Z J, XIE L L. A survey of research on cyber physical fusion system［J］. Journal of Automation, 2011, 37(10): 1157-1166. （in Chinese）
［2］ WANG W Y, LU Z. Cyber security in the smart grid: survey and challenges［J］. Computer Networks, 2013, 57(5): 1344-1371.
［3］ LIU Y, NING P, REITER M K. False data injection attacks against state estimation in electric power grids［J］. ACM Transactions on Information and System Security, 2011, 14(1): 21-32.
［4］ GU C J, PANIDA J, MEHUL M. Detecting false data injection attacks in ac state estimation［J］. IEEE Transactions on Smart Grid, 2015, 6(5): 2476-2483.
［5］ RAWAT D B, CHANDRA B. Detection of false data injection attacks in smart grid communication systems［J］. IEEE Signal Processing Letters, 2015, 22(10): 1652-1656.
［6］ TANG B, YAN J, KAY S, et al. Detection of false data injection attacks in smart grid under colored Gaussian noise［C］∥Proceedings of 2016 IEEE Conference on Communications and Network Security. Philadelphia, PA, US: IEEE, 2016: 172-179.
［7］ MO Y L, SINOPOLI B. False data injection attacks in cyber physical systems［C］∥Proceedings of the 1st Workshop on Secure Control Systems. Stockholm, Sweden:［s.n.］, 2010.
［8］ ZHANG H, QI Y F, WU J F, et al. DoS attack energy management against remote state estimation［J］. IEEE Transactions on Control of Network Systems, 2018, 5(1): 383-394.
［9］ MO Y L, SINOPOLI B. On the performance degradation of cyber-physical systems under stealthy integrity attacks［J］. IEEE Tran- sactions on Automatic Control, 2016, 61(9): 2618-2624.
［10］ KWON C, HWANG I . Reachability analysis for safety assurance of cyber-physical systems against cyber attacks［J］. IEEE Tran- sactions on Automatic Control, 2018, 63(7): 2272-2279.
［11］ YANG G H, YE D. Reliable H_∞ control of linear systems with adaptive mechanism［J］. IEEE Transactions on Automatic Control, 2010, 55(1): 242-247.
［12］ LI X J, YANG G H. Robust adaptive fault-tolerant control for uncertain linear systems with actuator failures［J］. IET Control Theory & Applications, 2012, 6(10): 1544-1551.
［13］ HAO L Y, PARK J H, YE D. Integral sliding mode fault-tolerant control for uncertain linear systems over networks with signals quantization［J］. IEEE Transactions on Neural Networks and Learning Systems, 2017, 28(9): 2088-2100.
［14］ SU L, YE D. A cooperative detection and compensation mechanism against Denial-of-Service attack for cyber-physical systems［J］. Information Sciences, 2018, 444: 122-134.
［15］ JIN X, HADDAD W M, YUCELEN T. An adaptive control architecture for mitigating sensor and actuator attacks in cyber-physical systems［J］. IEEE Transactions on Automatic Control, 2017, 62(11): 6058-6064.
［16］ AN L, YANG G H. Improved adaptive resilient control against sensor and actuator attacks［J］. Information Sciences, 2018, 423: 145-156.
［17］ WANG H Z, RUAN J Q, ZHOU B, et al. Dynamic data injection attack detection of cyber-physical power systems with uncertainties［J］. IEEE Transactions on Industrial Informatics, 2019,15(10): 5505-5518.
［18］ YE D, YANG G H. Delay-dependent adaptive reliable control for linear time-varying delay systems［J］. International Journal of Robust Nonlinear Control, 2009, 19(4): 462-479.

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