Improved event-triggered finite-time H_∞ control for neural networks subject to mixed-type communication attacks

References

• Chen, X., Wang, Y., & Hu, S. (2019). Event-triggered quantized H∞ control for networked control systems in the presence of denial-of-service jamming attacks. Nonlinear Analysis: Hybrid Systems, 33, 265–281. https://doi.org/10.1016/j.nahs.2019.03.005
   Web of Science ®Google Scholar
• Feng, Z., Shao, H., & Shao, L. (2020). Further results on event-triggered H∞ networked control for neural networks with stochastic cyber-attacks. Applied Mathematics and Computation, 386, Article 125431. https://doi.org/10.1016/j.amc.2020.125431
   Google Scholar
• Gao, H., Meng, X., & Chen, T. (2008). Stabilization of networked control systems with a new delay characterization. IEEE Transactions on Automatic Control, 53(9), 2142–2148. https://doi.org/10.1109/TAC.2008.930190
   Web of Science ®Google Scholar
• Gu, Z., Yue, D., Liu, J., & Ding, Z. (2017). H∞ tracking control of nonlinear networked systems with a novel adaptive event-triggered communication scheme. Journal of the Franklin Institute, 354(8), 3540–3553. https://doi.org/10.1016/j.jfranklin.2017.02.020
   Web of Science ®Google Scholar
• He, S., & Liu, F. (2013). Finite-time boundedness of uncertain time-delayed neural network with Markovian jumping parameters. Neurocomputing, 103(1), 87–92. https://doi.org/10.1016/j.neucom.2012.09.005
   Google Scholar
• Hu, S., & Yue, D. (2012). Event-triggered control design of linear networked systems with quantizations. ISA Transactions, 51(1), 153–162. https://doi.org/10.1016/j.isatra.2011.09.002
   PubMed Web of Science ®Google Scholar
• Jung, M., Lee, H., & Tani, J. (2018). Adaptive detrending to accelerate convolutional gated recurrent unit training for contextual video recognition. Neural Networks, 105, 356–370. https://doi.org/10.1016/j.neunet.2018.05.009
   PubMed Web of Science ®Google Scholar
• Kazemy, A., Saravanakumar, R., & Lam, J. (2021). Master-slave synchronization of neural networks subject to mixed-type communication attacks. Information Sciences, 560, 20–34. https://doi.org/10.1016/j.ins.2021.01.063
   Web of Science ®Google Scholar
• Li, F., & Shen, H. (2015). Finite-time H∞ synchronization control for semi-Markov jump delayed neural networks with randomly occurring uncertainties. Neurocomputing, 166, 447–454. https://doi.org/10.1016/j.neucom.2015.03.034
   Web of Science ®Google Scholar
• Li, F., Wu, L., & Shi, P. (2015). Stochastic stability of semi-Markovian jump systems with mode-dependent delays. International Journal of Robust and Nonlinear Control, 24(18), 3317–3330. https://doi.org/10.1002/rnc.3057
   Web of Science ®Google Scholar
• Li, H., Chen, Z., Wu, L., Lam, H.-K., & Du, H. (2017). Event-triggered fault detection of nonlinear networked systems. IEEE Transactions on Cybernetics, 47(4), 1041–1052. https://doi.org/10.1109/TCYB.2016.2536750
   PubMed Web of Science ®Google Scholar
• Li, T., Tang, X., Zhang, H., & Fei, S. (2019). Improved event-triggered control for networked control systems under stochastic cyber-attacks. Neurocomputing, 350, 33–43. https://doi.org/10.1016/j.neucom.2019.03.058
   Web of Science ®Google Scholar
• Liu, J., Suo, W., Xie, X., Yue, D., & Cao, J. (2021). Quantized control for a class of neural networks with adaptive event-triggered scheme and complex cyber-attacks. International Journal of Robust and Nonlinear Control, 31(10), 4705–4728. https://doi.org/10.1002/rnc.v31.10
   Web of Science ®Google Scholar
• Liu, J., Wu, Z.-G., Yue, D., & Park, J. H. (2021). Stabilization of networked control systems with hybrid-driven mechanism and probabilistic cyber-attacks. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 51(2), 943–953. https://doi.org/10.1109/TSMC.6221021
   Web of Science ®Google Scholar
• Liu, J., Zha, L., Xie, X., & Tian, E. (2018). Resilient observer-based control for networked nonlinear T-S fuzzy systems with hybrid-triggered scheme. Nonlinear Dynamics, 91(3), 2049–2061. https://doi.org/10.1007/s11071-017-4002-3
   Web of Science ®Google Scholar
• Luo, M., Mu, X., Wu, X., & Hu, Z. (2020). Event-triggered non-fragile finite-time H∞ control for semi-Markovian jump systems with actuator saturation. Journal of the Franklin Institute, 357(15), 10626–10647. https://doi.org/10.1016/j.jfranklin.2020.08.036
   Web of Science ®Google Scholar
• Mahmoud, M. S., Hamdan, M. M., & Baroudi, U. A. (2019). Modeling and control of cyber-physical systems subject to cyber attacks: A survey of recent advances and challenges. Neurocomputing, 338, 101–115. https://doi.org/10.1016/j.neucom.2019.01.099
   Web of Science ®Google Scholar
• Pan, R., Tan, Y., Du, D., & Fei, S. (2020). Adaptive event-triggered synchronization control for complex networks with quantization and cyber-attacks. Neurocomputing, 382, 249–258. https://doi.org/10.1016/j.neucom.2019.11.096
   Web of Science ®Google Scholar
• Park, P. G., Ko, J. W., & Jeong, C. (2011). Reciprocally convex approach to stability of systems with time-varying delays. Automatica, 47(1), 235–238. https://doi.org/10.1016/j.automatica.2010.10.014
   Web of Science ®Google Scholar
• Ren, C., & He, S. (2020). Finite-time stabilization for positive Markovian jumping neural networks. Applied Mathematics and Computation, 365, Article 124631. https://doi.org/10.1016/j.amc.2019.124631
   Google Scholar
• Shi, Y., Tian, E., Shen, S., & Zhao, X. (2021). Adaptive memory-event-triggered H-infinity control for network-based T-S fuzzy systems with asynchronous premise constraints. IET Control Theory and Applications, 15(4), 534–544. https://doi.org/10.1049/cth2.v15.4
   Web of Science ®Google Scholar
• Sun, Y.-C., & Yang, G.-H. (2019). Robust event-triggered model predictive control for cyber-physical systems under denial-of-service attacks. International Journal of Robust and Nonlinear Control, 29(14), 4797–4811. https://doi.org/10.1002/rnc.v29.14
   Web of Science ®Google Scholar
• Tao, J., Wu, Z.-G., Su, H., Wu, Y., & Zhang, D. (2018). Asynchronous and resilient filtering for Markovian jump neural networks subject to extended dissipativity. IEEE Transactions on Cybernetics, 49(7), 2504–2513. https://doi.org/10.1109/TCYB.6221036
   PubMed Web of Science ®Google Scholar
• Vembarasan, V., Balasubramaniam, P., & Chan, C. S. (2014). Non-fragile state observer design for neural networks with Markovian jumping parameters and time-delays. Nonlinear Analysis: Hybrid Systems, 14, 61–73. https://doi.org/10.1016/j.nahs.2014.05.006
   Web of Science ®Google Scholar
• Wang, B., & Zhu, Q. (2020). Stability analysis of discrete time semi-Markov jump linear systems. IEEE Transactions on Automatic Control, 65(12), 5415–5421. https://doi.org/10.1109/TAC.9
   Web of Science ®Google Scholar
• Wang, L., Shen, Y., & Ding, Z. (2015). Finite time stabilization of delayed neural networks. Neural Networks, 70, 74–80. https://doi.org/10.1016/j.neunet.2015.07.008
   PubMed Web of Science ®Google Scholar
• Wang, Y., Xu, S., Li, Y., Chu, Y., & Zhang, Z. (2021). Asynchronous finite-time state estimation for semi-Markovian jump neural networks with randomly occurred sensor nonlinearities. Neurocomputing, 432, 240–249. https://doi.org/10.1016/j.neucom.2020.12.027
   Web of Science ®Google Scholar
• Wang, Y., Zhuang, G., Chen, X., Wang, Z., & Chen, F. (2020). Dynamic event-based finite-time mixed H∞ and passive asynchronous filtering for T-S fuzzy singular Markov jump systems with general transition rates. Nonlinear Analysis: Hybrid Systems, 36, Article 100874. https://doi.org/10.1016/j.nahs.2020.100874
   Web of Science ®Google Scholar
• Xu, Y., Lu, H., Song, X., & Zhou, W. (2022). Event-triggered H∞ control for Markov jump delayed neural networks with redundant channels. Optimal Control Applications & Methods, 43(3), 804–824. https://doi.org/10.1002/oca.v43.3
   Web of Science ®Google Scholar
• Yan, Z., Song, Y., & Park, J. H. (2017). Finite-time stability and stabilization for stochastic Markov jump systems with mode-dependent time delays. ISA Transactions, 68, 141–149. https://doi.org/10.1016/j.isatra.2017.01.018
   PubMed Web of Science ®Google Scholar
• Zaman, A., Safarinejadian, B., & Birk, W. (2020). Security analysis and fault detection against stealthy replay attacks. International Journal of Control. https://doi.org/10.1080/00207179.2020.1862917
   Google Scholar
• Zha, L., Tian, E., Xie, X., Gu, Z., & Cao, J. (2018). Decentralized event-triggered H∞ control for neural networks subject to cyber-attacks. Information Sciences, 457–458, 141–155. https://doi.org/10.1016/j.ins.2018.04.018
   Web of Science ®Google Scholar
• Zhang, X.-M., Han, Q.-L., Ge, X., & Ding, D. (2018). An overview of recent developments in Lyapunov-Krasovskii functionals and stability criteria for recurrent neural networks with time-varying delays. Neurocomputing, 313, 392–401. https://doi.org/10.1016/j.neucom.2018.06.038
   Web of Science ®Google Scholar
• Zhao, L., & Yang, G.-H. (2020). Cooperative adaptive fault-tolerant control for multi-agent systems with deception attacks. Journal of the Franklin Institute, 357(6), 3419–3433. https://doi.org/10.1016/j.jfranklin.2019.12.032
   Web of Science ®Google Scholar
• Zhao, X., Liu, C., & Tian, E. (2020). Finite-horizon tracking control for a class of stochastic systems subject to input constraints and hybrid cyber attacks. ISA Transactions, 104, 93–100. https://doi.org/10.1016/j.isatra.2019.02.025
   PubMed Web of Science ®Google Scholar
• Zhu, Q. (2019). Stabilization of stochastic nonlinear delay systems with exogenous disturbances and the event-triggered feedback control. IEEE Transactions on Automatic Control, 64(9), 3764–3771. https://doi.org/10.1109/TAC.9
   Web of Science ®Google Scholar
• Zhu, Q., & Huang, T. (2021). H∞ control of stochastic networked control systems with time–varying delays: The event–triggered sampling case. International Journal of Robust and Nonlinear Control, 31(18), 9767–9781. https://doi.org/10.1002/rnc.v31.18
   Web of Science ®Google Scholar