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Sum-based event-triggered dynamic output feedback control for synchronization of fuzzy neural networks with deception attacks

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Abstract

This paper concerns with the event-based dynamic output feedback control for the synchronization of fuzzy neural networks under mixed delay and deception attacks. A weighted sum-based dynamic event-triggered mechanism (WSDETM) is developed to save the communication resources while preserving a satisfactory system performance. A dynamic output feedback controller (DOFC) is designed to achieve exponential synchronization of fuzzy neural networks. To reduce the data traffic, both communication channels from the sensor to DOFC and DOFC to Zero-Order Holder are subject to WSDETM. Different from the traditional deception attacks modeled by Bernoulli process, we adopt the more general Markov process modeling deception attacks. By using the cone-complimentarity linearization algorithm, the DOFC and WSDETM parameters are carried out. The effectiveness of the proposed method is demonstrated with two numerical cases.

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References

  1. Geng X, Zhou Z-H, Smith-Miles K (2008) Individual stable space: an approach to face recognition under uncontrolled conditions. IEEE Trans Neural Netw 19(8):1354–1368

    Article  Google Scholar 

  2. Qin C, Schlemper J, Caballero J, Price AN, Hajnal JV, Rueckert D (2019) Convolutional recurrent neural networks for dynamic MR image reconstruction. IEEE Trans Medi Imaging 38(1):280–290

    Article  Google Scholar 

  3. Wouafo H, Chavet C, Coussy P (2019) Clone-based encoded neural networks to design efficient associative memories. IEEE Trans Neural Netw Learn Syst 30(10):3186–3199

    Article  MathSciNet  Google Scholar 

  4. Pecora LM, Carroll TL (1990) Synchronization in chaotic systems. Phys Rev Lett 64:821–824

    Article  MATH  MathSciNet  Google Scholar 

  5. Fei Z, Guan C, Gao H (2018) Exponential synchronization of networked chaotic delayed neural network by a hybrid event trigger scheme. IEEE Trans Neural Netw Learn Syst 29(6):2558–2567

    Article  MathSciNet  Google Scholar 

  6. Li X, Zhang W, Fang J-A, Li H (2020) Event-triggered exponential synchronization for complex-valued memristive neural networks with time-varying delays. IEEE Trans Neural Netw Learn Syst 31(10):4104–4116

    Article  MathSciNet  Google Scholar 

  7. Xiao S-P, Lian H-H, Teo KL, Zeng H-B, Zhang X-H (2018) A new lyapunov functional approach to sampled-data synchronization control for delayed neural networks. J Franklin Instit 355(17):8857–8873

    Article  MATH  MathSciNet  Google Scholar 

  8. Zhang X-M, Han Q-L, Zhang B-L (2017) An overview and deep investigation on sampled-data-based event-triggered control and filtering for networked systems. IEEE Trans Ind Inform 13(1):4–16

    Article  MathSciNet  Google Scholar 

  9. Peng C, Sun H (2020) Switching-like event-triggered control for networked control systems under malicious denial of service attacks. IEEE Trans Autom Control 65(9):3943–3949

    Article  MATH  MathSciNet  Google Scholar 

  10. Ding L, Han Q-L, Ge X, Zhang X-M (2018) An overview of recent advances in event-triggered consensus of multiagent systems. IEEE Trans Cybern 48(4):1110–1123

    Article  Google Scholar 

  11. Hu S, Yue D, Cheng Z, Tian E, Xie X, Chen X (2021) Co-design of dynamic event-triggered communication scheme and resilient observer-based control under aperiodic DoS attacks. IEEE Trans Cybern 51(9):4591–4601

    Article  Google Scholar 

  12. Hu, S., Yue, D., Dou, C., Xie, X., Ma, Y., Ding, L.: Attack-resilient event-triggered fuzzy interval type-2 filter design for networked nonlinear systems under sporadic denial-of-service jamming attacks. IEEE Transactions on Fuzzy Systems, early access. (2020)

  13. Zhang L, Nguang SK, Ouyang D, Yan S (2020) Synchronization of delayed neural networks via integral-based event-triggered scheme. IEEE Trans Neural Netw Learn Syst 31(12):5092–5102

    Article  MathSciNet  Google Scholar 

  14. Yan S, Nguang SK, Gu Z (2021) \(H_{\infty }\) weighted integral event-triggered synchronization of neural networks with mixed delays. IEEE Trans Ind Inform 17(4):2365–2375

    Article  Google Scholar 

  15. Li Q, Shen B, Wang Z, Huang T, Luo J (2019) Synchronization control for a class of discrete time-delay complex dynamical networks: a dynamic event-triggered approach. IEEE Trans Cybern 49(5):1979–1986

    Article  Google Scholar 

  16. Hu W, Yang C, Huang T, Gui W (2020) A distributed dynamic event-triggered control approach to consensus of linear multiagent systems with directed networks. IEEE Trans Cybern 50(2):869–874

    Article  Google Scholar 

  17. Hu W, Liu L, Feng G (2017) Output consensus of heterogeneous linear multi-agent systems by distributed event-triggered/self-triggered strategy. IEEE Trans Cybern 47(8):1914–1924

    Article  Google Scholar 

  18. Liu C, Li H, Shi Y, Xu D (2020) Distributed event-triggered gradient method for constrained convex minimization. IEEE Trans Autom Control 65(2):778–785

    Article  MATH  MathSciNet  Google Scholar 

  19. Sahoo A, Xu H, Jagannathan S (2016) Adaptive neural network-based event-triggered control of single-input single-output nonlinear discrete-time systems. IEEE Trans Neural Netw Learn Syst 27(1):151–164

    Article  MathSciNet  Google Scholar 

  20. Yan S, Shen M, Nguang SK, Zhang G, Zhang L (2019) A distributed delay method for event-triggered control of T-S fuzzy networked systems with transmission delay. IEEE Trans Fuzzy Syst 27(10):1963–1973

    Article  Google Scholar 

  21. Li, H., Zhang, L., Zhang, X., Yu, J.: A switched integral-based event-triggered control of uncertain nonlinear time-delay system with actuator saturation. IEEE Transactions on Cybernetics, early access. (2021)

  22. Abdelrahim M, Postoyan R, Daafouz J (2015) Event-triggered control of nonlinear singularly perturbed systems based only on the slow dynamics. Automatica 52:15–22

    Article  MATH  MathSciNet  Google Scholar 

  23. Liu T, Jiang Z-P (2015) Event-based control of nonlinear systems with partial state and output feedback. Automatica 53:10–22

    Article  MATH  MathSciNet  Google Scholar 

  24. Yan S, Shen M, Nguang SK, Zhang G (2019) Event-triggered \(H_\infty\) control of networked control systems with distributed transmission delay. IEEE Trans Autom Control 65(10):4295–4301

    Article  MATH  MathSciNet  Google Scholar 

  25. Zhang L, Nguang SK, Yan S (2021) Event-triggered \(H_\infty\)control for networked control systems under denial-of-service attacks. Trans Instit Meas Control 43(5):1077–1087

    Article  Google Scholar 

  26. Song L, Nguang SK, Huang D (2018) Hierarchical stability conditions for a class of generalized neural networks with multiple discrete and distributed delays. IEEE Trans Neural Netw Learn Syst 30(2):636–642

    Article  MathSciNet  Google Scholar 

  27. Heemels WH, Donkers M, Teel AR (2012) Periodic event-triggered control for linear systems. IEEE Trans Autom Control 58(4):847–861

    Article  MATH  MathSciNet  Google Scholar 

  28. Zhang X-M, Han Q-L (2014) Event-triggered dynamic output feedback control for networked control systems. IET Control Theory & Appl 8(4):226–234

    Article  MathSciNet  Google Scholar 

  29. Guo G, Ding L, Han Q-L (2014) A distributed event-triggered transmission strategy for sampled-data consensus of multi-agent systems. Automatica 50(5):1489–1496

    Article  MATH  MathSciNet  Google Scholar 

  30. Liu J, Gu Y, Xie X, Yue D, Park JH (2019) Hybrid-driven-based \({H}_\infty\) control for networked cascade control systems with actuator saturations and stochastic cyber attacks. IEEE Trans Syst Man Cybern Syst 49(12):2452–2463

    Article  Google Scholar 

  31. Liu J, Wei L, Xie X, Tian E, Fei S (2018) Quantized stabilization for T-S fuzzy systems with hybrid-triggered mechanism and stochastic cyber-attacks. IEEE Trans Fuzzy Syst 26(6):3820–3834

    Article  Google Scholar 

  32. Kazemy, A., Lam, J., Zhang, X.-M.: Event-triggered output feedback synchronization of master-slave neural networks under deception attacks. IEEE Transactions on Neural Networks and Learning Systems, early access. (2020)

  33. Peng C, Tian Y-C (2007) Networked \(H_{\infty }\) control of linear systems with state quantization. Inform Sci 177(24):5763–5774

    Article  MATH  MathSciNet  Google Scholar 

  34. Yue D, Tian E, Han Q-L (2012) A delay system method for designing event-triggered controllers of networked control systems. IEEE Trans Autom Control 58(2):475–481

    Article  MATH  MathSciNet  Google Scholar 

  35. Tan Y, Liu Y, Niu B, Fei S (2020) Event-triggered synchronization control for T-S fuzzy neural networked systems with time delay. J Franklin Instit 357(10):5934–5953

    Article  MATH  MathSciNet  Google Scholar 

  36. Seuret A, Gouaisbaut F (2013) Wirtinger-based integral inequality: application to time-delay systems. Automatica 49(9):2860–2866

    Article  MATH  MathSciNet  Google Scholar 

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Acknowledgements

This work is supported by the National Natural Science Foundation of China (No.11671284), the Zhejiang Lab’s International Talent Fund for Young Professionals and the Grant SCITLAB(SCITLAB-1016) of Intelligent Terminal Key Laboratory of SiChuan Province.

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Authors and Affiliations

  1. College of Computer Science, Chongqing University, Chongqing, 400044, China

    Duo Zhang & Deqiang Ouyang

  2. School of Mathematical Sciences, University of Electronic Science and Technology of China, Chengdu, 611731, Sichuan, China

    Duo Zhang & Lan Shu

  3. Intelligent Terminal Key Laboratory of Sichuan Province, Yibin, 644000, China

    Deqiang Ouyang

  4. College of Mathematics and System Science, Xinjiang University, Urumqi, 830046, Xinjiang, China

    Cheng Hu

  5. School of Information Science and Engineering, Chengdu University, Chengdu, 611040, Sichuan, China

    Kaibo Shi

  6. Australian AI Institute, Faculty of Engineering and Information Technology, University of Technology Sydney, Ultimo, NSW, 2007, Australia

    Shiping Wen

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  1. Duo Zhang
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  3. Lan Shu
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  4. Cheng Hu
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Correspondence to Deqiang Ouyang.

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Zhang, D., Ouyang, D., Shu, L. et al. Sum-based event-triggered dynamic output feedback control for synchronization of fuzzy neural networks with deception attacks. Neural Comput & Applic 35, 10221–10237 (2023). https://doi.org/10.1007/s00521-023-08231-7

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  • DOI: https://doi.org/10.1007/s00521-023-08231-7

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