logo

SCIENTIA SINICA Informationis, Volume 51 , Issue 8 : 1302(2021) https://doi.org/10.1360/SSI-2020-0166

Memory-based event-triggered secure state estimation of cyber-physical systems

More info
  • ReceivedAug 16, 2020
  • AcceptedDec 5, 2020
  • PublishedAug 9, 2021

Abstract


Funded by

国家自然科学基金(61473156,61773200)

江苏省自然科学基金青年项目(BK20200769)

江苏省高等学校自然科学研究面上项目(20KJB510045)


References

[1] Derler P, Lee E A, Vincentelli A S. Modeling cyber-physical systems. Proc IEEE, 2011, 100: 13-28. Google Scholar

[2] Yu X, Xue Y. Smart Grids: A Cyber-Physical Systems Perspective. Proc IEEE, 2016, 104: 1058-1070 CrossRef Google Scholar

[3] Chai T. Industrial process control systems: research status and development direction. Sci Sin-Inf, 2016, 46: 1003-1015 CrossRef Google Scholar

[4] Ye P, You J, Qiu X, et al. Status and development trend of motion performance in parallel robot. J Nanjing Univ Aeronaut Astronaut, 2020, 52: 363-377.. Google Scholar

[5] Deshmukh S, Natarajan B, Pahwa A. State Estimation Over a Lossy Network in Spatially Distributed Cyber-Physical Systems. IEEE Trans Signal Process, 2014, 62: 3911-3923 CrossRef Google Scholar

[6] Cao X, Cheng P, Chen J. Cognitive Radio Based State Estimation in Cyber-Physical Systems. IEEE J Sel Areas Commun, 2014, 32: 489-502 CrossRef Google Scholar

[7] Mo Y, Garone E, Casavola A, et al. False data injection attacks against state estimation in wireless sensor networks. In 49th IEEE Conference on Decision and Control (CDC), 2010: 5967-5972. Google Scholar

[8] Hu L, Wang Z, Han Q L. State estimation under false data injection attacks: Security analysis and system protection. Automatica, 2018, 87: 176-183 CrossRef Google Scholar

[9] Guan Y, Ge X. Distributed attack detection and secure estimation of networked cyber-physical systems against false data injection attacks and jamming attacks. IEEE Trans Signal Inf Process Networks, 2017, 4: 48-59. Google Scholar

[10] Yan S, Shen M, Nguang S K, et al. Event-triggered $~H_~{\infty}~$ control of networked control systems with distributed transmission delay. IEEE Trans on Autom Contr, 2019, 65: 4295-4301. Google Scholar

[11] Tian E, Wang Z, Zou L. Automatica, 2019, 107: 296-305 CrossRef Google Scholar

[12] Mo Y, Jiang Z H, Li H. A kind of biomimetic control method to anthropomorphize a redundant manipulator for complex tasks. Sci China Technol Sci, 2020, 63: 14-24 CrossRef Google Scholar

[13] You K, Xie L. Survey of recent progress in network control system. Acta Autom Sin, 2013, 39: 101-118.. Google Scholar

[14] Weimer J, Araújo J, Johansson K H. Distributed event-triggered estimation in networked systems. IFAC Proc Volumes, 2012, 45: 178-185. Google Scholar

[15] Huang J, Shi D, Chen T. Event-Triggered State Estimation With an Energy Harvesting Sensor. IEEE Trans Automat Contr, 2017, 62: 4768-4775 CrossRef Google Scholar

[16] Liu J, Tang J, Fei S. Event-based state estimation for delayed neural network systems with quantization. Sci Sin-Inf, 2016, 46: 1555-1568 CrossRef Google Scholar

[17] Li Q, Shen B, Wang Z. Event-triggered H state estimation for state-saturated complex networks subject to quantization effects and distributed delays. J Franklin Institute, 2018, 355: 2874-2891 CrossRef Google Scholar

[18] Yang W, Lei L, Yang C. Event-based distributed state estimation under deception attack. Neurocomputing, 2017, 270: 145-151 CrossRef Google Scholar

[19] Mousavi S H, Marquez H J. Integral-based event triggering controller design for stochastic LTI systems via convex optimisation. Int J Control, 2016, 89: 1416-1427 CrossRef Google Scholar

[20] Wang X, Fei Z, Gao H. Integral-Based Event-Triggered Fault Detection Filter Design for Unmanned Surface Vehicles. IEEE Trans Ind Inf, 2019, 15: 5626-5636 CrossRef Google Scholar

[21] Atkinson K E. An introduction to numerical analysis. Wiley, New York, 1978. Google Scholar

[22] Peng C, Zhang J. IET Control Theor & Appl, 2015, 9: 1384-1391 CrossRef Google Scholar

[23] Liu J, Wei L, Xie X, Yue D. Distributed event-triggered state estimators design for sensor networked systems with deception attacks. IET Contr Theory Appl, 2018, 13: 2783-2791. Google Scholar

[24] Gu Z, Zhou X, Zhang T. et al. Event-triggered filter design for nonlinear cyber-physical systems subject to deception attacks. ISA Trans, 2019, doi.org/10.1016/j.isatra.2019.02.036. Google Scholar

[25] Peng C, Han Q L. Output-based event-triggered $H_{\infty}$ control for sampled-data control systems with nonuniform sampling. In American Contr Conference, Washington, U.S.A., 2013: 1727-1732. Google Scholar

[26] Seuret A, Gouaisbaut F, Ariba Y. Complete quadratic Lyapunov functionals for distributed delay systems. Automatica, 2015, 62: 168-176 CrossRef Google Scholar

[27] Boyd S, Ghaoui L E L, Feron E, et al. Linear matrix inequalities in system and control theory. SIAM, Philadelphia, PA, 1994. Google Scholar

[28] Park P G, Ko J W, Jeong C. Reciprocally convex approach to stability of systems with time-varying delays. Automatica, 2011, 47: 235-238 CrossRef Google Scholar

[29] Ding R, Hu W, Yang Y. Rotating consensus control of double-integrator multi-agent systems with event-based communication. Sci China Inf Sci, 2020, 63: 1-10. Google Scholar

  • Figure 1

    (Color online) (a) State responses and (b) triggering time instants and intervals under the estimator without considering the false data injection attacks

  • Figure 2

    (Color online) (a) State responses and (b) triggering time instants and intervals under the estimator considering the false data injection attacks

  • Figure 3

    (Color online) (a) State responses and (b) triggering time instants and intervals under the conventional event-triggered scheme

  • Figure 4

    (Color online) (a) State responses and (b) triggering time instants and intervals under the memory-based event-triggered scheme

  • Table 1   Comparison of simulation with false data injection attacks
    Cases of designing estimator Parameters Attack signals Simulation results
    Case 1 Parameter 1 $g(t)=\tanh(e(t))$, $\chi_1=0.6$ Figure 1
    Case 2 Parameter 2 $g(t)=\tanh(e(t))$, $\chi_1=0.6$ Figure 2
  • Table 2   $L$, $\Phi$ and $\mathscr~N$ (number of triggering times) under different $h$
    $h=0.01$ $h=0.1$ $h=0.2$
    $L$ $\left[~{\begin{array}{*{20}{c}}~-1.1206~&~1.6454~\\ ~~~~1.3809~&~~-2.1832~\end{array}}~\right]$ $\left[~{\begin{array}{*{20}{c}}~~-1.9679~~&~~0.5243\\ ~~~~2.2300~&~~-1.2867~\end{array}}~\right]$ $\left[~{\begin{array}{*{20}{c}}~~-1.5413~~&~~0.2454~\\ ~~~~1.9691~&~~-1.2863~\end{array}}~\right]$
    $\Phi$ $\left[~{\begin{array}{*{20}{c}}~304.6578~&~-122.0442~\\ ~-122.0442~&~938.2760~\end{array}}~\right]$ $\left[~{\begin{array}{*{20}{c}}~90.4011~&~62.9115~\\ ~~~62.9115~&~223.4876~\end{array}}~\right]$ $\left[~{\begin{array}{*{20}{c}}~~40.8925~~&~31.6947~\\ ~~~31.6947~~&~91.3931\end{array}}~\right]$
    $\mathscr~N$ 162 135 122
qqqq

Contact and support