Event-triggered stabilization for networked control systems under random occurring deception attacks

Dong Xu; Xinling Li; Weipeng Tai; Jianping Zhou; Dong Xu; Xinling Li; Weipeng Tai; Jianping Zhou

doi:10.3934/mbe.2023039

Mathematical Biosciences and Engineering

2023, Volume 20, Issue 1: 859-878. doi: 10.3934/mbe.2023039

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Research article

Event-triggered stabilization for networked control systems under random occurring deception attacks

1.
Research Institute of Information Technology, Anhui University of Technology, Ma'anshan 243000, China
2.
School of Computer Science & Technology, Anhui University of Technology, Ma'anshan 243032, China

Academic Editor: Yang Kuang

Received: 16 September 2022 Revised: 04 October 2022 Accepted: 13 October 2022 Published: 18 October 2022

This paper copes with event-triggered stabilization for networked control systems subject to deception attacks. A new switched event-triggered scheme (ETS) is designed by introducing a term regarding the last triggering moment in the trigger condition. This increases the difficulty of triggering, thus reducing trigger times compared to some existing ETSs. Furthermore, to cater for actual deception attack behavior, the occurrence of deception attacks is assumed to be a time-dependent stochastic variable that obeys the Bernoulli distribution with probability uncertainty. By means of a piecewise-defined Lyapunov function, a sufficient condition is developed to assure that the close-loop system under deception attacks is exponentially stable in regards to mean square. On the basis of this, a joint design of the desired trigger and feedback-gain matrices is presented. Finally, a simulation example is given to confirm the validity of the design method.
- networked control systems,
- deception attacks,
- lyapunov function,
- stabilization,
- event-triggered control
Citation: Dong Xu, Xinling Li, Weipeng Tai, Jianping Zhou. Event-triggered stabilization for networked control systems under random occurring deception attacks[J]. Mathematical Biosciences and Engineering, 2023, 20(1): 859-878. doi: 10.3934/mbe.2023039

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Abstract

This paper copes with event-triggered stabilization for networked control systems subject to deception attacks. A new switched event-triggered scheme (ETS) is designed by introducing a term regarding the last triggering moment in the trigger condition. This increases the difficulty of triggering, thus reducing trigger times compared to some existing ETSs. Furthermore, to cater for actual deception attack behavior, the occurrence of deception attacks is assumed to be a time-dependent stochastic variable that obeys the Bernoulli distribution with probability uncertainty. By means of a piecewise-defined Lyapunov function, a sufficient condition is developed to assure that the close-loop system under deception attacks is exponentially stable in regards to mean square. On the basis of this, a joint design of the desired trigger and feedback-gain matrices is presented. Finally, a simulation example is given to confirm the validity of the design method.

References

[1]	Y. Song, Z. Wang, D. Ding, G. Wei, Robust model predictive control under redundant channel transmission with applications in networked DC motor systems, Int. J. Robust Nonlinear Control, 26 (2016), 3937–3957. https://doi.org/10.1002/rnc.3542 doi: 10.1002/rnc.3542
[2]	J. Liang, C. Gong, Y. Hou, M. Yu, W. Wang, Application of networked discrete event system theory on intelligent transportation systems, Control Theory Technol., 19 (2021), 236–248. https://doi.org/10.1007/s11768-020-00002-2 doi: 10.1007/s11768-020-00002-2
[3]	L. Zhang, H. Gao, O. Kaynak, Network-induced constraints in networked control systems–A survey, IEEE Trans. Ind. Inf., 9 (2013), 403–416. https://doi.org/10.1109/TII.2012.2219540 doi: 10.1109/TII.2012.2219540
[4]	X. Liang, J. Xu, Control for networked control systems with remote and local controllers over unreliable communication channel, Automatica, 98 (2018), 86–94. https://doi.org/10.1016/j.automatica.2018.09.015 doi: 10.1016/j.automatica.2018.09.015
[5]	A. Kazemy, R. Saravanakumar, J. Lam, Master-slave synchronization of neural networks subject to mixed-type communication attacks, Inf. Sci., 560 (2021), 20–34. https://doi.org/10.1016/j.ins.2021.01.063 doi: 10.1016/j.ins.2021.01.063
[6]	J. Tian, R. Tan, X. Guan, Z. Xu, T. Liu, Moving target defense approach to detecting stuxnet-like attacks, IEEE Trans. Smart Grid, 11 (2019), 291–300. https://doi.org/10.1109/TSG.2019.2921245 doi: 10.1109/TSG.2019.2921245
[7]	H. Goyel, K. S. Swarup, Data integrity attack detection using ensemble based learning for cyber physical power systems, IEEE Trans. Smart Grid, (2022), In press. https://10.1109/TSG.2022.3199305.
[8]	Q. Wang, H. Yang, A survey on the recent development of securing the networked control systems, Syst. Sci. Control Eng., 17 (2019), 54–64. https://doi.org/10.1080/21642583.2019.1566800 doi: 10.1080/21642583.2019.1566800
[9]	X. M. Zhang, Q. L. Han, X. Ge, D. Ding, L. Ding, D. Yue, et al., Networked control systems: A survey of trends and techniques, IEEE/CAA J. Autom. Sin., 7 (2019), 1–17. https://doi.org/10.1109/JAS.2019.1911651 doi: 10.1109/JAS.2019.1911651
[10]	X. Jin, S. Lü, C. Deng, M. Chadli, Distributed adaptive security consensus control for a class of multi-agent systems under network decay and intermittent attacks, Inf. Sci., 547 (2021), 88–102. https://doi.org/10.1016/j.ins.2020.08.013 doi: 10.1016/j.ins.2020.08.013
[11]	Y. Yuan, H. Yuan, L. Guo, H. Yang, S. Sun, Resilient control of networked control system under DoS attacks: A unified game approach, IEEE Trans. Ind. Inf., 12 (2016), 1786–1794. https://doi.org/10.1109/TII.2016.2542208 doi: 10.1109/TII.2016.2542208
[12]	K. Paridari, N. OMahony, A. E. D. Mady, R. Chabukswar, M. Boubekeur, H. Sandberg, A framework for attack-resilient industrial control systems: Attack detection and controller reconfiguration, Proc. IEEE, 106 (2017), 113–128. https://doi.org/10.1109/JPROC.2017.2725482 doi: 10.1109/JPROC.2017.2725482
[13]	D. Du, C. Zhang, H. Wang, X. Li, H. Hu, T. Yang, Stability analysis of token-based wireless networked control systems under deception attacks, Inf. Sci., 459 (2018), 168–182. https://doi.org/10.1016/j.ins.2018.04.085 doi: 10.1016/j.ins.2018.04.085
[14]	Z. Hu, F. Deng, Y. Su, J. Zhang, S. Hu, Security control of networked systems with deception attacks and packet dropouts: A discrete-time approach, J. Franklin Inst., 358 (2021), 8193–8206. https://doi.org/10.1016/j.jfranklin.2021.08.015 doi: 10.1016/j.jfranklin.2021.08.015
[15]	X. Gao, F. Deng, C. Y. Su, P. Zeng, Protocol-based fuzzy control of networked systems under joint deception attacks, IEEE Trans. Fuzzy Syst., (2022), In press. https://doi.org/10.1109/TFUZZ.2022.3194365
[16]	T. Chen, B. A. Francis, Optimal Sampled-Data Control Systems, Springer Science & Business Media, (2012), 209–220. https://doi.org/10.1007/978-1-4471-3037-6 doi: 10.1007/978-1-4471-3037-6
[17]	N. Gunasekaran, M. S. Ali, S. Arik, H. A. Ghaffar, A. A. Z. Diab, Finite-time and sampled-data synchronization of complex dynamical networks subject to average dwell-time switching signal, Neural Networks, 149 (2022), 137–145. https://doi.org/10.1016/j.neunet.2022.02.013 doi: 10.1016/j.neunet.2022.02.013
[18]	R. Vadivel, P. Hammachukiattikul, N. Gunasekaran, R. Saravanakumar, H. Dutta, Strict dissipativity synchronization for delayed static neural networks: An event-triggered scheme, Chaos Solitons Fractals, 150 (2021), 111212. https://doi.org/10.1016/j.chaos.2021.111212 doi: 10.1016/j.chaos.2021.111212
[19]	N. Gunasekaran, Y. H. Joo, Robust sampled-data fuzzy control for nonlinear systems and its applications: Free-weight matrix method, IEEE Trans. Fuzzy Syst., 27 (2019), 2130–2139. https://doi.org/10.1109/TFUZZ.2019.2893566 doi: 10.1109/TFUZZ.2019.2893566
[20]	Z. M. Li, X. H. Chang, J. H. Park, Quantized static output feedback fuzzy tracking control for discrete-time nonlinear networked systems with asynchronous event-triggered constraints, IEEE Trans. Syst. Man Cybern.: Syst., 51 (2021), 3820–3831. https://doi.org/10.1109/TSMC.2019.2931530 doi: 10.1109/TSMC.2019.2931530
[21]	M. Dlala, S. O. Alrashidi, Rapid exponential stabilization of Lotka-McKendrick's equation via event-triggered impulsive control, Math. Biosci. Eng., 18 (2021), 9121–9131. https://doi.org/10.3934/mbe.2021449 doi: 10.3934/mbe.2021449
[22]	P. Selvaraj, O. Kwon, S. H. Lee, R. Sakthivel, Equivalent-input-disturbance estimator-based event-triggered control design for master-slave neural networks, Neural Networks, 143 (2021), 413–424. https://doi.org/10.1016/j.neunet.2021.06.023 doi: 10.1016/j.neunet.2021.06.023
[23]	C. Ge, X. Liu, Y. Liu, C. Hua, Event-triggered exponential synchronization of the switched neural networks with frequent asynchronism, IEEE Trans. Neural Networks Learn. Syst., (2022), In press. https://doi.org/10.1109/TFUZZ.2022.3194365.
[24]	C. Wang, Z. Ma, S. Tong, Adaptive fuzzy output-feedback event-triggered control for fractional-order nonlinear system, Math. Biosci. Eng., 19 (2022), 12334–12352. https://doi.org/10.3934/mbe.2022575 doi: 10.3934/mbe.2022575
[25]	D. Xu, Z. Li, G. Cui, W. Hao, Distributed fixed-time secondary control of an islanded microgrid via distributed event-triggered mechanism, Int. J. Control, (2022), In press. https://doi.org/10.1080/00207179.2022.2032832.
[26]	Z. Wu, J. Xiong, M. Xie, Improved event-triggered control for networked control systems subject to deception attacks, J. Franklin Inst., 358 (2021), 2229–2252. https://doi.org/10.1016/j.jfranklin.2020.12.018 doi: 10.1016/j.jfranklin.2020.12.018
[27]	Y. Sun, J. Yu, X. Yu, H. Gao, Decentralized adaptive event-triggered control for a class of uncertain systems with deception attacks and its application to electronic circuits, IEEE Trans. Circuits Syst. Ⅰ Regul. Pap., 67 (2020), 5405–5416. https://doi.org/10.1109/TCSI.2020.3027678 doi: 10.1109/TCSI.2020.3027678
[28]	J. Lian, Y. Han, Switching-like event-triggered control for networked Markovian jump systems under deception attack, IEEE Trans. Circuits Syst. Ⅱ Express Briefs, 68 (2021), 3271–3275. https://doi.org/10.1109/TCSII.2021.3065679 doi: 10.1109/TCSII.2021.3065679
[29]	B. Shen, Z. Wang, D. Wang, Q. Li, State-saturated recursive filter design for stochastic time-varying nonlinear complex networks under deception attacks, IEEE Trans. Neural Networks Learn. Syst., 31 (2019), 3788–3800. https://doi.org/10.1109/TNNLS.2019.2946290 doi: 10.1109/TNNLS.2019.2946290
[30]	J. Cheng, Y. Wang, J. H. Park, J. Cao, K. Shi, Static output feedback quantized control for fuzzy Markovian switching singularly perturbed systems with deception attacks, IEEE Trans. Fuzzy Syst., 30 (2021), 1036–1047. https://doi.org/10.1109/TFUZZ.2021.3052104 doi: 10.1109/TFUZZ.2021.3052104
[31]	A. Selivanov, E. Fridman, Event-triggered $\mathcal{H}_{\infty}$ control: A switching approach, IEEE Trans. Autom. Control, 61 (2016), 3221–3226. https://doi.org/10.1109/TAC.2015.2508286 doi: 10.1109/TAC.2015.2508286
[32]	J. Lunze, D. Lehmann, A state-feedback approach to event-based control, Automatica, 46 (2010), 211–215. https://doi.org/10.1016/j.automatica.2009.10.035 doi: 10.1016/j.automatica.2009.10.035
[33]	Z. Yan, X. Huang, J. Cao, Variable-sampling-period dependent global stabilization of delayed memristive neural networks based on refined switching event-triggered control, Sci. China Inf. Sci., 63 (2020), 212201. https://doi.org/10.1007/s11432-019-2664-7 doi: 10.1007/s11432-019-2664-7
[34]	S. Ding, X. Xie, Y. Liu, Event-triggered static/dynamic feedback control for discrete-time linear systems, Inf. Sci., 524 (2020), 33–45. https://doi.org/10.1016/j.ins.2020.03.044 doi: 10.1016/j.ins.2020.03.044
[35]	Z. Yan, X. Huang, Y. Fan, J. Xia, H. Shen, Threshold-function-dependent quasi-synchronization of delayed memristive neural networks via hybrid event-triggered control, IEEE Trans. Syst. Man Cybern.: Syst., 51 (2021), 6712–6722. https://doi.org/10.1109/TSMC.2020.2964605 doi: 10.1109/TSMC.2020.2964605
[36]	W. Wu, L. He, J. Zhou, Z. Xuan, S. Arik, Disturbance-term-based switching event-triggered synchronization control of chaotic Lurie systems subject to a joint performance guarantee, Commun. Nonlinear Sci. Numer. Simul., 115 (2022), 106774. https://doi.org/10.1016/j.cnsns.2022.106774 doi: 10.1016/j.cnsns.2022.106774
[37]	L. Xu, D. Xu, Mean square exponential stability of impulsive control stochastic systems with time-varying delay, Phys. Lett. A, 373 (2009), 328–333. https://doi.org/10.1016/j.physleta.2008.11.029 doi: 10.1016/j.physleta.2008.11.029
[38]	K. Gu, J. Chen, V. L. Kharitonov, Stability of Time-Delay Systems, Boston, MA: Birkhauser, 2003. https://doi.org/10.1007/978-1-4612-0039-0
[39]	K. Zhou, P. P. Khargonekar, Robust stabilization of linear systems with norm-bounded time-varying uncertainty, Syst. Control Lett., 10 (1988), 17–20. https://doi.org/10.1016/0167-6911(88)90034-5 doi: 10.1016/0167-6911(88)90034-5
[40]	J. Zhou, J. H. Park, Q. Ma, Non-fragile observer-based $\mathcal{H}_{\infty}$ control for stochastic time-delay systems, Appl. Math. Comput., 291 (2016), 69–83. https://doi.org/10.1016/j.amc.2016.06.024 doi: 10.1016/j.amc.2016.06.024
[41]	E. Fridman, A refined input delay approach to sampled-data control, Automatica, 46 (2010), 421–427. https://doi.org/10.1016/j.automatica.2009.11.017 doi: 10.1016/j.automatica.2009.11.017
[42]	Y. Wang, G. Yang, $\mathcal{H}_{\infty}$ control of networked control systems with delay and packet disordering via predictive method, in Proceedings of the 2007 American Control Conference, (2010), 1021–1026. https://doi.org/10.1109/ACC.2007.4282390
[43]	J. Wang, Y. Zhang, L. Su, J. H. Park, H. Shen, Model-based fuzzy filtering for discrete-time Semi-Markov jump nonlinear systems using semi-markov kernel, IEEE Trans. Fuzzy Syst., 30 (2022), 2289–2299. https://doi.org/10.1109/TFUZZ.2021.3078832 doi: 10.1109/TFUZZ.2021.3078832
[44]	D. Tong, C. Xu, Q. Chen, W. Zhou, Y. Xu, Sliding mode control for nonlinear stochastic systems with Markovian jumping parameters and mode-dependent time-varying delays, Nonlinear Dyn., 100 (2020), 1343–1358. https://doi.org/10.1007/s11071-020-05597-4 doi: 10.1007/s11071-020-05597-4
[45]	H. Shen, X. Hu, J. Wang, J. Cao, W. Qian, Non-fragile $H_{\infty}$ synchronization for markov jump singularly perturbed coupled neural networks subject to double-layer switching regulation, IEEE Trans. Neural Networks Learn. Syst., (2022), In press. https://doi.org/10.1109/TNNLS.2021.3107607.
[46]	M. S. Ali, N. Gunasekaran, R. Agalya, Y. H. Joo, Non-fragile synchronisation of mixed delayed neural networks with randomly occurring controller gain fluctuations, Int. J. Syst. Sci., 49 (2018), 3354–3364. https://doi.org/10.1080/00207721.2018.1540730 doi: 10.1080/00207721.2018.1540730

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