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Finite-Time H Sampled-Data Reliable Control for a Class of Markovian Jump Systems with Randomly Occurring Uncertainty via T-S Fuzzy Model

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Abstract

The paper analyzes finite-time H sampled-data reliability control for nonlinear continuous time Markovian jump systems with randomly occurring uncertainty on account of T-S fuzzy model. In particular, the transition rates of the Markovian jump systems have both the upper bound and lower bound. Meanwhile, a new Lyapunov-Krasovskii functional (LKF) is considered, which fully captures the available characteristics of real sampling period, and a sampled-data controller with nonlinear actuator failures is designed. Based on the integral inequality technique, some less conservative conditions are proposed such that the stochastic fuzzy system is reliable in the sense, which satisfies finite-time bounded and certain H performance level γ. Additionally, some numerical examples can illustrate the effectiveness of the results.

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References

  1. Zhang Q L, Zhang J Y, and Wang Y Y, Sliding-mode control for singular Markovian jump systems with Brownian motion based on stochastic sliding mode surface, IEEE Trans. Man. Cybern. Syst, 2019, 49: 494–505.

    Article  Google Scholar 

  2. Guan C X, Fei Z Y, Feng Z G, et al., Stability and stabilization of singular Markovian jump systems by dynamic event-triggered control strategy, Nonlinear Anal.: Hybrid Syst., 2020, 38: 100943.

    MathSciNet  MATH  Google Scholar 

  3. Zhang P P, Hu J, Zhang H X, et al., H sliding mode control for Markovian jump systems with randomly occurring uncertainties and repeated scalar nonlinearities via delay-fractioning method, ISA Trans., 2020, 101: 10–22.

    Article  Google Scholar 

  4. Pan S Y, Zhou J, and Ye Z Y, Event-triggered dynamic output feedback control for networked Markovian jump systems with partly unknown transition rates, Math. Comput. Simulat., 2021, 181: 539–561.

    Article  MathSciNet  Google Scholar 

  5. Tong S C, Sun K K, and Sui S, Observer-based adaptive fuzzy decentralized optimal control design for strict-feedback nonlinear large-scale systems, IEEE Trans. Fuzzy Syst., 2018, 26(2): 569–584.

    Article  Google Scholar 

  6. Li Y M, Sun K K, and Tong S C, Observer-based adaptive fuzzy fault-tolerant optimal control for SISO nonlinearsystems, IEEE Trans. Cybern., 2019, 49(2): 649–661.

    Article  Google Scholar 

  7. Takagi T and Sugeno M, Fuzzy identification of systems and its application to modeling and control, IEEE Trans. Man. Cybern. Syst., 1985, 15: 116–132.

    Article  Google Scholar 

  8. Li X H, Lu D K, Zhang W, et al., Sensor fault estimation and fault-tolerant control for a class of Takagi-Sugeno Markovian jump systems with partially unknown transition rates based on the reduced-order observer, Journal of Systems Science and Complexity, 2018, 31(6): 1405–1422.

    Article  MathSciNet  Google Scholar 

  9. Liang X Y, Xia J W, Chen G L, et al., Sampled-data control for fuzzy Markovian jump systems with actuator saturation, IEEE. Access, 2019, 7: 180417–180427.

    Article  Google Scholar 

  10. Guan C X, Fei Z Y, and Park P G, Modified looped functional for sampled-data control of T-S fuzzy Markovianjumpsystems, IEEE Trans. Fuzzy Syst., DOI: https://doi.org/10.1109/TFUZZ.2020.3003498.

  11. Liu Y Y and Ting H, Robust l2l fuzzy filtering for nonlinear stochastic systems with infinite Markov jump, Journal of Systems Science and Complexity, 2020, 33(4): 1023–1039.

    Article  MathSciNet  Google Scholar 

  12. Ren J C, He G X, and Fu J, Robust H sliding mode control for nonlinear stochastic T-S fuzzy singular Markovian jump systems with time-varying delays, Inf. Sci., 2020, 535: 42–63.

    Article  MathSciNet  Google Scholar 

  13. Xu Y H, Wang Y Q, Zhuang G M, et al., Reliable mixed H/passive control for T-S fuzzy semi-Markovian jump systems under different event-triggered schemes, IET Control Theory Appl., 2020, 14(4): 594–604.

    Article  MathSciNet  Google Scholar 

  14. Du P H, Pan Y N, Li H Y, et al., Nonsingular finite-time event-triggered fuzzy control for large-scale nonlinear systems, IEEE Trans. Fuzzy Syst., 2020, DOI: https://doi.org/10.1109/TFUZZ.2020.2992632.

  15. Cui Y F, Hu J, Wu Z H, et al., Finite-time sliding mode control for networked singular Markovian jump systems with packet losses: A delay-fractioning scheme, Neurocomputing, 2020, 385: 48–62.

    Article  Google Scholar 

  16. Cao Z R and Niu Y G, Finite-time stochastic boundedness of Markovian jump systems: A sliding-mode-based hybrid designmethod, Nonlinear Anal: Hybrid Syst., 2020, 36: 100862.

    MathSciNet  MATH  Google Scholar 

  17. Kang W, Gao Q F, Cao M L, et al., Finite-time control for Markovian jump systems subject to randomly occurring quantization, Appl. Math. Comput., 2020, 385: 125402.

    MathSciNet  MATH  Google Scholar 

  18. Chen Z H, Tan J, Wang X F, et al., Decentralized finite-time l2l tracking control for a class of interconnected Markovian jump system with actuator saturation, ISA Trans., 2020, 96: 69–80.

    Article  Google Scholar 

  19. Jiang B P, Karimi H R, Kao Y G, et al., Takagi-Sugeno model-based sliding mode observer design for finite-time synthesis of semi-Markovian jump systems, IEEE Trans. Syst. Man Cybern., 2019, 49(7): 1505–1515.

    Article  Google Scholar 

  20. Lin W J, He Y, Zhang C K, et al., Stochastic finite-time H state estimation for discrete-time semi-Markovian jump neural networks with time-varying delays, IEEE Trans. Neural Netw. Learn. Syst., 2020, 31(12): 5456–5467.

    Article  MathSciNet  Google Scholar 

  21. Pan Y N, Du P H, Xue H, et al., Singularity-free fixed-time fuzzy control for robotic systems with user-defined performance, IEEE Trans. Fuzzy Syst., 2020, DOI: https://doi.org/10.1109/TFUZZ.2020.2999746.

  22. Liang H J, Liu G L, Zhang H G, et al., Neural-network-based event-triggered adaptive control of nonaffine nonlinear multiagent systems with dynamic uncertainties, IEEE Trans. Neural Netw. Learn. Syst., 2020, DOI: https://doi.org/10.1109/TNNLS.2020.3003950.

  23. Syed Ali M, Agalya R, Shekher V, et al., Non-fragile sampled data control for stabilization of non-linearmulti-agent system with additive time varying delays, Markovian jump and uncertain parameters, Nonlinear Anal: Hybrid Syst., 2020, 36: 100830.

    MathSciNet  MATH  Google Scholar 

  24. Park J M and Park P G, Sampled-data control for continuous-time Markovian jump linear systems via a fragmented-delay state and its state-space model, J. Franklin Inst., 2019, 356: 5073–5086.

    Article  MathSciNet  Google Scholar 

  25. Muthukumar P, Arunagirinathan S, and Lakshmanan S, Nonfragile sampled-data control for uncertain networked control systems with additive time-varying delays, IEEE Trans. Cybern., 2019, 49: 1512–1523.

    Article  Google Scholar 

  26. Liu Y A, Xia J W, Meng B, et al., Extended dissipative synchronization for semi-Markov jump complex dynamic networks via memory sampled-data control scheme, J. Franklin Inst., 2020, 357: 10900–10920.

    Article  MathSciNet  Google Scholar 

  27. Zeng H B, Teo K L, He Y, et al., Sampled-data-based dissipative control of T-S fuzzy systems, Appl. Math. Model, 2019, 65: 415–427.

    Article  MathSciNet  Google Scholar 

  28. Lin X Z, Zhang W L, Yang Z L, et al., Finite-time boundedness of switched systems with time-varying delays via sampled-data control, Int. J. Robust Nonlinear Control, 2020, 30: 2953–2976.

    Article  MathSciNet  Google Scholar 

  29. Xu T S, Xia J W, Wang S X, et al., Extended dissipativity-based non-fragile sampled-data control of fuzzy Markovian jump systems with incomplete transition rates, Appl. Math. Model, 2020, 380: 125258.

    MathSciNet  MATH  Google Scholar 

  30. Chen G L, Sun J, and Chen J, Passivity-based robust sampled-data control for Markovian jump systems, IEEE Trans. Man. Cybern. Syst., 2020, 50(7): 2671–2684.

    Article  Google Scholar 

  31. Ding K and Zhu Q X, Impulsive method to reliable sampled-data control for uncertain fractional-order memristive neural networks with tochastic sensor faults and its applications, Nonlinear Dyn., 2020, 100: 2595–2608.

    Article  Google Scholar 

  32. Kuppusamy S and Joo Y H, Stabilization of interval type-2 fuzzy-based reliable sampled-data control systems, IEEE Trans. Cybern., 2020, DOI: https://doi.org/10.1109/TCYB.2020.3001609.

  33. Ma Y C and Liu Y F, Finite-time H sliding mode control for uncertain singular stochastic system with actuator faults and bounded transition probabilities, Nonlinear Anal.: Hybrid Syst., 2019, 33: 52–75.

    MathSciNet  MATH  Google Scholar 

  34. Sun G H, Xu S D, and Li Z, Finite-ime Fuzzy sampled-data control for nonlinear flexible spacecraft with stochastic actuator failures, IEEE Trans. Industrial Electronics, 2017, 64(5): 3851–3861.

    Article  Google Scholar 

  35. Sakthivel R, Karimi H R, Joby M, et al., Resilient sampled-data control for Markovian jump systems with an adaptive fault-tolerant mechanism, IEEE Trans. Circuits Syst. II: Express Briefs, 2017, 64(11): 1312–1316.

    Article  Google Scholar 

  36. Liu H and Zhou G P, Finite-time sampled-data control for switching T-S fuzzy systems, Neurocomputing, 2015, 166: 294–300.

    Article  Google Scholar 

  37. Kao Y G, Xie J, Zhang L X, et al., A sliding mode approach to robust stabilisation of Markovian jump linear time-delay systems with generally incomplete transition rates, Nonlinear Anal.: Hybrid Syst., 2015, 17: 70–80.

    MathSciNet  MATH  Google Scholar 

  38. Ding Y C and Liu H, Stability analysis of continuous-time Markovian jump time-delay systems with time-varying transition rates, J. Franklin Inst., 2016, 353: 2418–2430.

    Article  MathSciNet  Google Scholar 

  39. Lian J, Li S Y, and Liu J, T-S fuzzy control of positive Markov jump nonlinear systems, IEEE Trans. Fuzzy Syst., 2018, 26: 2374–2383.

    Article  Google Scholar 

  40. Li J Y, Huang X L, and Li Z C, Exponential stabilization for fuzzy sampled-data system based on a unified framework and its application, J. Franklin Inst., 2017, 354: 5302–5327.

    Article  MathSciNet  Google Scholar 

  41. Yan H, Wang T, Zhang H, et al., Event-triggered H control for uncertain networked T-S fuzzy systems with time delay, Neurocomputing, 2015, 157: 273–279.

    Article  Google Scholar 

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Correspondence to Yuanyuan Liu or Yuechao Ma.

Additional information

This research was supported by the National Natural Science Foundation of China under Grant No. 61273004, and the Natural Science Foundation of Hebei Province No. F2018203099.

This paper was recommended for publication by Editor LIU Kang-Zhi.

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Liu, Y., Zhang, Y. & Ma, Y. Finite-Time H Sampled-Data Reliable Control for a Class of Markovian Jump Systems with Randomly Occurring Uncertainty via T-S Fuzzy Model. J Syst Sci Complex 35, 860–887 (2022). https://doi.org/10.1007/s11424-021-0220-3

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  • DOI: https://doi.org/10.1007/s11424-021-0220-3

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