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Quantization-Based Event-Triggering Fault Detection Filtering for T–S Fuzzy Singular Semi-Markovian Jump Systems Against Multi-cyber-attacks

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Abstract

This paper delves into the problem of asynchronous fault detection filter (AFDF) for Takagi–Sugeno (T–S) fuzzy singular semi-Markovian jump systems (FSSJSs) with multi-cyber-attacks via dynamic event-triggering mechanism (ETM) and double-quantized schemes (DQS). First, a new design structure is devised by integrating the ETM and DQS into a unified framework to effectively alleviate the communication pressure. In particular, the event-triggered threshold (ETT) is dynamically regulated in line with system information, and not only the data from the plant to the filter but also that from the filter to the plant, are both quantized using different logarithmic quantizers. Second, the hybrid cyber-attacks platform is adopted, of which including the random deception attacks (DAs), random replay attacks (RAs) and DoS attack. Third, leveraging the principles of Lyapunov theory, some sufficient conditions are derived to guarantee the exponential admissibility (EA) of the resulting error dynamics and their adherence to a prescribed \(\mathcal {H}_{\infty }\) performance index. Through the utilization of several matrix inequalities, the desired filtering gains are given. Finally, the effectiveness of the achieved results is demonstrated by two examples.

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References

  1. Mu, Y., Zhang, H., **, R., Gao, Z.: State and fault estimations for discrete-time T–S fuzzy systems with sensor and actuator faults. IEEE Trans. Circuits Syst. Express Briefs 68(10), 3326–3330 (2021)

    Article  Google Scholar 

  2. Zhang, R., Zeng, D., Park, J.H., Lam, H.K., **e, X.: Fuzzy sampled-data control for synchronization of T–S fuzzy reaction-diffusion neural networks with additive time-varying delays. IEEE Trans. Cybern. 51(5), 2384–2397 (2021)

    Article  Google Scholar 

  3. Tian, Y., Wang, Z.: Finite-time extended dissipative filtering for singular T–S fuzzy systems with nonhomogeneous Markov jumps. IEEE Trans. Cybern. 52(6), 4574–4584 (2022)

    Article  Google Scholar 

  4. Feng, Z., Zhang, H., Lam, H.K.: New results on dissipative control for a class of singular Takagi–Sugeno fuzzy systems with time delay. IEEE Trans. Fuzzy Syst. 30(7), 2466–2475 (2022)

    Article  Google Scholar 

  5. Meng, A., Lam, H.K., Liu, F., Yang, Y.: Filter design for positive T–S fuzzy continuous-time systems with time delay using piecewise-linear membership functions. IEEE Trans. Fuzzy Syst. 29(9), 2521–2531 (2021)

    Article  Google Scholar 

  6. Sun, H., Han, H., Sun, J., Yang, H., Qiao, J.: Security control of sampled-data T–S fuzzy systems subject to cyberattacks and successive packet losses. IEEE Trans. Fuzzy Syst. 31(4), 1178–1188 (2023)

    Article  Google Scholar 

  7. Ding, H., Qiao, J., Huang, W., Yu, T.: Event-triggered online learning fuzzy-neural robust control for furnace temperature in municipal solid waste incineration process. IEEE Trans. Autom. Sci. Eng. (2023). https://doi.org/10.1109/TASE.2023.3294420

    Article  Google Scholar 

  8. Yang, D., Feng, Q., Zong, G.: Asynchronous bumpless transfer finite-time \(\cal{H} _{\infty }\) control for Markovian jump systems with application to circuit system. IEEE Trans. Circuits Syst. Express Briefs 69(12), 4929–4933 (2022)

    Article  Google Scholar 

  9. Li, M., Chen, X., Liu, M., Zhang, Y., Zhang, H.: Asynchronous adaptive fault-tolerant sliding-mode control for T–S fuzzy singular Markovian jump systems with uncertain transition rates. IEEE Trans. Cybern. 52(1), 544–555 (2022)

    Article  Google Scholar 

  10. Qi, W., Yang, Y., Park, J.H., Yan, H., Wu, Z.G.: Protocol-based synchronization of stochastic jum** inertial neural networks under image encryption application. IEEE Trans. Neural Netw. Learn. Syst. (2023). https://doi.org/10.1109/TNNLS.2023.3300270

    Article  Google Scholar 

  11. Ren, J., Sun, Q., Fu, J.: Robust sliding mode control for stochastic singular Markovian jump systems with unmatched one-sided Lipschitz nonlinearities. J. Franklin Inst. 359(5), 1821–1851 (2022)

    Article  MathSciNet  Google Scholar 

  12. Cao, Z., Niu, Y., Zou, Y.: Adaptive neural sliding mode control for singular semi-Markovian jump systems against actuator attacks. IEEE Trans. Syst. Man Cybern. Syst. 51(3), 1523–1533 (2021)

    Google Scholar 

  13. Wang, L., Wu, Z., Shen, Y.: Asynchronous mean stabilization of positive jump systems with piecewise-homogeneous Markov chain. IEEE Trans. Circuits Syst. Express Briefs 68(10), 3266–3270 (2021)

    Article  Google Scholar 

  14. Li, F., Xu, S., Zhang, B.: Resilient asynchronous \(H_{\infty }\) control for discrete-time Markov jump singularly perturbed systems based on hidden Markov model. IEEE Trans. Syst. Man Cybern. Syst. 50(8), 2860–2869 (2020)

    Google Scholar 

  15. Tian, Y., Wang, Z.: A switched vertices approach to stability analysis of delayed Markov jump systems with time-varying transition rates. IEEE Trans. Circuits Syst. Express Briefs 69(1), 139–143 (2022)

    Article  Google Scholar 

  16. Lin, W., He, Y., Zhang, C., Wang, L., Wu, M.: Event-triggered fault detection filter design for discrete-time memristive neural networks with time delays. IEEE Trans. Cybern. 52(5), 3359–3369 (2022)

    Article  Google Scholar 

  17. Liu, K., Ji, Z.: Dynamic event-triggered consensus of general linear multi-agent systems with adaptive strategy. IEEE Trans. Circuits Syst. Express Briefs 69(8), 3440–3444 (2022)

    Article  Google Scholar 

  18. Wang, Y., Zhao, J.: Periodic event-triggered sliding mode control for switched uncertain T–S fuzzy systems with a logistic adaptive event-triggering scheme. IEEE Trans. Fuzzy Syst. 30(10), 4115–4126 (2022)

    Article  Google Scholar 

  19. Gong, C., Zhu, G., Shi, P.: Adaptive event-triggered and double quantized consensus of leader-follower multiagent systems with semi-Markovian jump parameters. IEEE Trans. Netw. Sci. Eng. 51(9), 5867–5879 (2022)

    Google Scholar 

  20. Guo, H., Pang, Z., Sun, J., Li, J.: An output-coding-based detection scheme against replay attacks in cyber-physical systems. IEEE Trans. Circuits Syst. Express Briefs 68(10), 3306–3310 (2021)

    Article  Google Scholar 

  21. Chen, Q., Chang, X.: Resilient filter of nonlinear network systems with dynamic event-triggered mechanism and hybrid cyber attack. Appl. Math. Comput. 434, 127419 (2022)

    MathSciNet  Google Scholar 

  22. Zhang, L., Lam, H.K., Sun, Y., Liang, H.: Fault detection for fuzzy semi-Markov jump systems based on interval type-2 fuzzy approach. IEEE Trans. Fuzzy Syst. 28(10), 2375–2388 (2020)

    Article  Google Scholar 

  23. Yang, S., Wen, Y., Qiao, B., Wang, K., Su, X.: Fault detection filter design for nonlinear singular systems with Markovian jump parameters. IEEE Syst. J. 15(3), 4168–4176 (2021)

    Article  Google Scholar 

  24. Zhang, Q., Yan, H., Wang, M., Li, Z., Chang, Y.: Asynchronous fault detection filter design for T–S fuzzy singular systems via dynamic event-triggered scheme. IEEE Trans. Fuzzy Syst. (2022). https://doi.org/10.1109/TFUZZ.2022.3193456

    Article  Google Scholar 

  25. Abbaszadeh, M., Marquez, H.J.: Nonlinear observer design for one-sided Lipschitz systems. In: Proceedings of the American Control Conference, pp. 5284–5289 (2010)

  26. Fu, M., **e, L.: The sector bound approach to quantized feedback control. IEEE Trans. Autom. Control 50(11), 1698–1711 (2005)

    Article  MathSciNet  Google Scholar 

  27. Liu, J., Wang, Y., Cao, J., Yue, D., **e, X.: Secure adaptive-event-triggered filter design with input constraint and hybrid cyber attack. IEEE Trans. Cybern. 51(8), 4000–4010 (2022)

    Article  Google Scholar 

  28. Lu, A., Yang, G.: Input-to-state stabilizing control for cyberphysical systems with multiple transmission channels under denial of service. IEEE Trans. Autom. Control 63(6), 1813–1820 (2018)

    Article  MathSciNet  Google Scholar 

  29. Zhu, L., Wang, Y., Zhuang, G., Song, G.: Dynamic-memory event-based asynchronous dissipative filtering for T–S fuzzy singular semi-Markov jump systems against multi-cyber attacks. Appl. Math. Comput. 431, 127352 (2022)

    MathSciNet  Google Scholar 

Download references

Funding

This study was supported by National Natural Science Foundation of China (Grant No. 11661028), the Natural Science Foundation of Guangxi (Grant No. 2020GXNSFAA159141).

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Authors and Affiliations

  1. School of Mathematics and Statistics, Guilin University of Technology, Guilin, 541004, Guangxi, People’s Republic of China

    Linghuan Kong & Mengzhuo Luo

  2. School of Mathematics and Statistics, Guangxi Normal University, Guilin, 541006, Guangxi, People’s Republic of China

    Jun Cheng

  3. Department of Computer Science, Faculty of Computing and Information Technology, King Abdulaziz University, 21589, Jeddah, Saudi Arabia

    Iyad Katib

  4. School of Information Science and Engineering, Chengdu University, Chengdu, 610106, Sichuan, People’s Republic of China

    Kaibo Shi

  5. School of Mathematical Sciences, University of Electronic Science and Technology of China, Chengdu, 611731, Sichuan, People’s Republic of China

    Shouming Zhong

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  1. Linghuan Kong
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Correspondence to Mengzhuo Luo.

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Kong, L., Luo, M., Cheng, J. et al. Quantization-Based Event-Triggering Fault Detection Filtering for T–S Fuzzy Singular Semi-Markovian Jump Systems Against Multi-cyber-attacks. Int. J. Fuzzy Syst. 26, 1328–1346 (2024). https://doi.org/10.1007/s40815-023-01669-w

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