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Dynamic Event-Triggered Fuzzy Adaptive Control for Non-strict-Feedback Stochastic Nonlinear Systems with Injection and Deception Attacks

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Abstract

A dynamic event-triggered adaptive fuzzy control scheme for a class of non-strict feedback stochastic nonlinear systems with injection and deception attacks is developed in this article. Compared with the static event-triggered control (SETC), the dynamic event-triggered control (DETC) guarantees a longer trigger interval, which means it can be more efficient in saving system communication resources. In addition, both injection and deception attacks are considered to realize the information security of the controlled system, and a single parameter adaptive law is constructed for the control strategy to reduce the calculation scheme. This scheme ensures uniform boundedness of all signals within the closed-loop system in probability and the output is not violating the given constraint. Finally, a digital simulation and physical model comparison examples are presented and discussed, which visualize the superiority of the above scheme.

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Data Availability

The data used to support the findings of this study are available from the corresponding author upon request.

References

  1. Min, H., Xu, S., Zhang, B., Ma, Q.: Output-feedback control for stochastic nonlinear systems subject to input saturation and time-varying delay. IEEE Trans. Autom. Control 64(1), 359–364 (2019). https://doi.org/10.1109/TAC.2018.2828084

    Article  MathSciNet  MATH  Google Scholar 

  2. Yu, Z., Yan, H., Li, S., Dong, Y.: Approximation-based adaptive tracking control for switched stochastic strict-feedback nonlinear time-delay systems with sector-bounded quantization input. IEEE Trans. Syst. Man Cybern. 48(12), 2145–2157 (2018). https://doi.org/10.1109/TSMC.2017.2721430

    Article  Google Scholar 

  3. Sui, S., Chen, C.L.P., Tong, S.: Fuzzy adaptive finite-time control design for nontriangular stochastic nonlinear systems. IEEE Trans. Fuzzy Syst. 27(1), 172–184 (2019). https://doi.org/10.1109/TFUZZ.2018.2882167

    Article  Google Scholar 

  4. Wang, F., Chen, B., Sun, Y., Lin, C.: Finite time control of switched stochastic nonlinear systems. Fuzzy Sets Syst. 365, 140–152 (2019). https://doi.org/10.1016/j.fss.2018.04.016

    Article  MathSciNet  MATH  Google Scholar 

  5. Huo, X., Ma, L., Zhao, X., Zong, G.: Observer-based fuzzy adaptive stabilization of uncertain switched stochastic nonlinear systems with input quantization. J. Franklin Inst. 356(4), 1789–1809 (2019). https://doi.org/10.1016/j.jfranklin.2018.11.022

    Article  MathSciNet  MATH  Google Scholar 

  6. Wang, F., Chen, B., Sun, Y., Gao, Y., Lin, C.: Finite-time fuzzy control of stochastic nonlinear systems. IEEE Trans. Cybern. 50(6), 2617–2626 (2020). https://doi.org/10.1109/TCYB.2019.2925573

    Article  Google Scholar 

  7. Ma, H., Zhou, Q., Bai, L., Liang, H.: Observer-based adaptive fuzzy fault-tolerant control for stochastic nonstrict-feedback nonlinear systems with input quantization. IEEE Trans. Syst. Man Cybern. 49(2), 287–298 (2019). https://doi.org/10.1109/TSMC.2018.2833872

    Article  Google Scholar 

  8. Wang, N., Tao, F., Fu, Z., Song, S.: Adaptive fuzzy control for a class of stochastic strict feedback high-order nonlinear systems with full-state constraints. IEEE Trans. Syst. Man Cybern. 52(1), 205–213 (2022). https://doi.org/10.1109/TSMC.2020.2996635

    Article  Google Scholar 

  9. Li, K., Li, Y., Zong, G.: Adaptive fuzzy fixed-time decentralized control for stochastic nonlinear systems. IEEE Trans. Fuzzy Syst. 29(11), 3428–3440 (2021). https://doi.org/10.1109/TFUZZ.2020.3022570

    Article  Google Scholar 

  10. Ning, Z., Yu, J., Pan, Y., Li, H.: Adaptive event-triggered fault detection for fuzzy stochastic systems with missing measurements. IEEE Trans. Fuzzy Syst. 26(4), 2201–2212 (2018). https://doi.org/10.1109/TFUZZ.2017.2780799

    Article  Google Scholar 

  11. Zhu, Q.: Stabilization of stochastic nonlinear delay systems with exogenous disturbances and the event-triggered feedback control. IEEE Trans. Autom. Control 64(9), 3764–3771 (2019). https://doi.org/10.1109/TAC.2018.2882067

    Article  MathSciNet  MATH  Google Scholar 

  12. Sui, S., Chen, C.L.P., Tong, S.: Event-trigger-based finite-time fuzzy adaptive control for stochastic nonlinear system with unmodeled dynamics. IEEE Trans. Fuzzy Syst. 29(7), 1914–1926 (2021). https://doi.org/10.1109/TFUZZ.2020.2988849

    Article  Google Scholar 

  13. Zou, W., Ahn, C.K., Xiang, Z.: Event-triggered consensus tracking control of stochastic nonlinear multiagent systems. IEEE Syst. J. 13(4), 4051–4059 (2019). https://doi.org/10.1109/JSYST.2019.2910723

    Article  Google Scholar 

  14. Zou, W., Shi, P., Xiang, Z., Shi, Y.: Consensus tracking control of switched stochastic nonlinear multiagent systems via event-triggered strategy. IEEE Trans. Neural Netw. Learn. Syst. 31(3), 1036–1045 (2020). https://doi.org/10.1109/TNNLS.2019.2917137

    Article  MathSciNet  Google Scholar 

  15. Ma, H., Li, H., Liang, H., Dong, G.: Adaptive fuzzy event-triggered control for stochastic nonlinear systems with full state constraints and actuator faults. IEEE Trans. Fuzzy Syst. 27(11), 2242–2254 (2019). https://doi.org/10.1109/TFUZZ.2019.2896843

    Article  Google Scholar 

  16. Li, B., Xia, J., Zhang, H., Shen, H., Wang, Z.: Event-triggered adaptive fuzzy tracking control for stochastic nonlinear systems. J. Franklin Inst. 357(14), 9505–9522 (2020). https://doi.org/10.1016/j.jfranklin.2020.07.023

    Article  MathSciNet  MATH  Google Scholar 

  17. Liu, Z., Wang, J., Chen, C.L.P., Zhang, Y.: Event trigger fuzzy adaptive compensation control of uncertain stochastic nonlinear systems with actuator failures. IEEE Trans. Fuzzy Syst. 26(6), 3770–3781 (2018). https://doi.org/10.1109/TFUZZ.2018.2848909

    Article  Google Scholar 

  18. Li, F., Liu, Y.: Event-triggered stabilization for continuous-time stochastic systems. IEEE Trans. Autom. Control 65(10), 4031–4046 (2020). https://doi.org/10.1109/TAC.2019.2953081

    Article  MathSciNet  MATH  Google Scholar 

  19. Ruan, X., Feng, J., Xu, C., Wang, J.: Observer-based dynamic event-triggered strategies for leader-following consensus of multi-agent systems with disturbances. IEEE Trans. Netw. Sci. Eng. 7(4), 3148–3158 (2020). https://doi.org/10.1109/TNSE.2020.3017493

    Article  MathSciNet  Google Scholar 

  20. Guo, X.-G., Liu, P.-M., Wang, J.-L., Ahn, C.K.: Event-triggered adaptive fault-tolerant pinning control for cluster consensus of heterogeneous nonlinear multi-agent systems under aperiodic DoS attacks. IEEE Trans. Netw. Sci. Eng. 8(2), 1941–1956 (2021). https://doi.org/10.1109/TNSE.2021.3077766

    Article  MathSciNet  Google Scholar 

  21. Luo, Y., Zhu, W., Cao, J., Rutkowski, L.: Event-triggered finite-time guaranteed cost H-infinity consensus for nonlinear uncertain multi-agent systems. IEEE Trans. Netw. Sci. Eng. 9(3), 1527–1539 (2022). https://doi.org/10.1109/TNSE.2022.3147254

    Article  MathSciNet  Google Scholar 

  22. Mahmoud, M.S., Karaki, B.J.: Output-synchronization of discrete-time multiagent systems: a cooperative event-triggered dissipative approach. IEEE Trans. Netw. Sci. Eng. 8(1), 114–125 (2021). https://doi.org/10.1109/TNSE.2020.3029078

    Article  MathSciNet  Google Scholar 

  23. Gu, Z., Shi, P., Yue, D., Yan, S., Xie, X.: Fault estimation and fault-tolerant control for networked systems based on an adaptive memory-based event-triggered mechanism. IEEE Trans. Netw. Sci. Eng. 8(4), 3233–3241 (2021). https://doi.org/10.1109/TNSE.2021.3107935

    Article  MathSciNet  Google Scholar 

  24. Guo, H., Liu, J., Ahn, C.K., Wu, Y., Li, W.: Dynamic event-triggered impulsive control for stochastic nonlinear systems with extension in complex networks. IEEE Trans. Circ. Syst. I 69(5), 2167–2178 (2022). https://doi.org/10.1109/TCSI.2022.3141583

    Article  Google Scholar 

  25. Luo, S., Deng, F.: On event-triggered control of nonlinear stochastic systems. IEEE Trans. Autom. Control 65(1), 369–375 (2020). https://doi.org/10.1109/TAC.2019.2916285

    Article  MathSciNet  MATH  Google Scholar 

  26. Wang, L., Chen, C.L.P.: Reduced-order observer-based dynamic event-triggered adaptive NN control for stochastic nonlinear systems subject to unknown input saturation. IEEE Trans. Neural Netw. Learn. Syst. 32(4), 1678–1690 (2021). https://doi.org/10.1109/TNNLS.2020.2986281

    Article  MathSciNet  Google Scholar 

  27. Li, X., Zhou, Q., Li, P., Li, H., Lu, R.: Event-triggered consensus control for multi-agent systems against false data-injection attacks. IEEE Trans. Cybern. 50(5), 1856–1866 (2020). https://doi.org/10.1109/TCYB.2019.2937951

    Article  Google Scholar 

  28. Dong, L., Xu, H., Wei, X., Hu, X.: Security correction control of stochastic cyber-physical systems subject to false data injection attacks with heterogeneous effects. ISA Trans. (2021). https://doi.org/10.1016/j.isatra.2021.05.015

    Article  Google Scholar 

  29. Hu, L., Wang, Z., Han, Q., Liu, X.: State estimation under false data injection attacks: security analysis and system protection. Automatica 87, 176–183 (2018). https://doi.org/10.1016/j.automatica.2017.09.028

    Article  MathSciNet  MATH  Google Scholar 

  30. Ning, Z., Wang, T., Zhang, K.: Dynamic event-triggered security control and fault detection for nonlinear systems with quantization and deception dttack. Inf Sci 594, 43–59 (2022). https://doi.org/10.1016/j.ins.2022.02.019

    Article  Google Scholar 

  31. Ding, D., Wang, Z., Han, Q.L., Wei, G.: Security control for discrete-time stochastic nonlinear systems subject to deception attacks. IEEE Trans. Syst. Man Cybern. 48(5), 779–789 (2018). https://doi.org/10.1109/TSMC.2016.2616544

    Article  Google Scholar 

  32. Kazemy, A., Lam, J., Zhang, X.: Event-triggered output feedback synchronization of master-slave neural networks under deception attacks. IEEE Trans. Neural Netw. Learn. Syst. 33(3), 952–961 (2022). https://doi.org/10.1109/TNNLS.2020.3030638

    Article  MathSciNet  Google Scholar 

  33. Shen, B., Wang, Z., Wang, D., Li, Q.: State-saturated recursive filter design for stochastic time-varying nonlinear complex networks under deception attacks. IEEE Trans. Neural Netw. Learn. Syst. 31(10), 3788–3800 (2020). https://doi.org/10.1109/TNNLS.2019.2946290

    Article  MathSciNet  Google Scholar 

  34. Song, H., Ding, D., Dong, H., Han, Q.L.: Distributed maximum correntropy filtering for stochastic nonlinear systems under deception attacks. IEEE Trans. Cybern. (2020). https://doi.org/10.1109/TCYB.2020.3016093

    Article  Google Scholar 

  35. Liu, L., Sun, H., Ma, L., Zhang, J., Bo, Y.: Quasi-consensus control for a class of time-varying stochastic nonlinear time-delay multiagent systems subject to deception attacks. IEEE Trans. Syst. Man Cybern. 51(11), 6863–6873 (2021). https://doi.org/10.1109/TSMC.2020.2964826

    Article  Google Scholar 

  36. Wei, B., Tian, E., Liu, J., Zhao, X.: Probabilistic-constrained tracking control for stochastic time-varying systems under deception attacks: a round-robin protocol. J. Franklin Inst. 358(17), 9135–9157 (2021). https://doi.org/10.1016/j.jfranklin.2021.09.021

    Article  MathSciNet  MATH  Google Scholar 

  37. Yan, H., Wang, J., Zhang, H., Shen, H., Zhan, X.: Event-based security control for stochastic networked systems subject to attacks. IEEE Trans. Syst. Man Cybern. 50(11), 4643–4654 (2020). https://doi.org/10.1109/TSMC.2018.2856819

    Article  Google Scholar 

  38. Yang, Y., Huang, J., Su, X., Wang, K., Li, G.: Adaptive control of second-order nonlinear systems with injection and deception attacks. IEEE Trans. Syst. Man Cybern. 52(1), 574–581 (2022). https://doi.org/10.1109/TSMC.2020.3003801

    Article  Google Scholar 

  39. Zhao, K., Song, Y., Meng, W., Chen, C.L.P., Chen, L.: Low-cost approximation-based adaptive tracking control of output-constrained nonlinear systems. IEEE Trans. Neural Netw. Learn. Syst. 32(11), 4890–4900 (2021). https://doi.org/10.1109/TNNLS.2020.3026078

    Article  MathSciNet  Google Scholar 

  40. Zhang, J., Xiang, X., Lapierre, L., Zhang, Q., Li, W.: Approach-angle-based three-dimensional indirect adaptive fuzzy path following of under-actuated AUV with input saturation. Appl. Ocean Res. 107, 102486 (2021). https://doi.org/10.1016/j.apor.2020.102486

    Article  Google Scholar 

  41. Yi, X., Liu, K., Dimarogonas, D.V., Johansson, K.H.: Dynamic event-triggered and self-triggered control for multi-agent systems. IEEE Trans. Autom. Control 64(8), 3300–3307 (2019). https://doi.org/10.1109/TAC.2018.2874703

    Article  MathSciNet  MATH  Google Scholar 

  42. Liu, Y., Gong, M., Liu, L., Tong, S., Chen, C.L.P.: Fuzzy observer constraint based on adaptive control for uncertain nonlinear MIMO systems with time-varying state constraints. IEEE Trans. Cybern. 51(3), 1380–1389 (2021). https://doi.org/10.1109/TCYB.2019.2933700

    Article  Google Scholar 

  43. Gao, M., Zhao, J., Zhuang, G., Sun, Z.: Finite-time state-feedback stabilization of high-order stochastic nonlinear systems with an asymmetric output constraint. Int. J. Adapt. Control Signal Process. 36(7), 1691–1701 (2022). https://doi.org/10.1002/acs.3421

    Article  MathSciNet  Google Scholar 

  44. 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). https://doi.org/10.1016/j.amc.2022.127352

    Article  MathSciNet  MATH  Google Scholar 

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Acknowledgements

This work was supported in part by the National Natural Science Foundation of China under Grant 61603003; in part by the Open Research Project of the State Key Laboratory of Industrial Control Technology, Zhejiang University, China, under Grant ICT2022B39; in part by the Program for Academic Top Notch Talents of University Disciplines under Grant gxbjZD21.

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Author notes

  1. Furong He, Xiaoguang He and Jing Li have contributed equally to this work.

Authors and Affiliations

  1. The University Key Laboratory of Intelligent Perception and Computing of Anhui Province, Anqing Normal University, Anqing, 246011, China

    Jian Wu & Furong He

  2. College of Equipment Management and Support, Engineering University of PAP, Xi’an, 710086, China

    Xiaoguang He

  3. The School of Mathematics and Statistics, Xidian University, Xi’an, 710071, China

    Jing Li

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  1. Jian Wu
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  2. Furong He
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Correspondence to Jian Wu.

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Wu, J., He, F., He, X. et al. Dynamic Event-Triggered Fuzzy Adaptive Control for Non-strict-Feedback Stochastic Nonlinear Systems with Injection and Deception Attacks. Int. J. Fuzzy Syst. 25, 1144–1155 (2023). https://doi.org/10.1007/s40815-022-01429-2

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