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access icon free Event-based fault detection for non-linear networked systems with multi-data transmission and output quantisation

  • Author(s): Xiao-Lei Wang 1  and  Guang-Hong Yang 1, 2
    • View affiliations
  • Source: Volume 11, Issue 16, 03 November 2017, p. 2698 – 2706
    DOI:  10.1049/iet-cta.2017.0293 , Print ISSN 1751-8644, Online ISSN 1751-8652
© The Institution of Engineering and Technology
Received 17/03/2017, Accepted 10/07/2017, Revised 03/06/2017, Published 13/07/2017

This study is concerned with the event-triggered fault detection (FD) filter design problem for the discrete-time non-linear systems with multi-data transmission and output quantisation. To reduce the transmission data size, the output signals are quantified by a logarithmic quantiser. Compared with the existing results, past output measurements are first introduced for packaging and transmission only when the event condition is satisfied. Based on this consideration, more data can be used by the FD filter, while using the traditional method, the FD filter can only use the current data. Finally, some simulation results show that the proposed design method can achieve a better FD performance than the existing method with current output measurements transmission.

Inspec keywords: networked control systems; discrete time systems; control system synthesis; nonlinear control systems; fault tolerant control

Other keywords: discrete-time nonlinear systems; multidata transmission; event-triggered fault detection; transmission data size; output quantisation; nonlinear networked systems; event condition; FD filter design problem; event-based fault detection; logarithmic quantiser

Subjects: Control system analysis and synthesis methods; Nonlinear control systems; Discrete control systems

References

    1. 1)
      • 20. Yan, H.C., Wang, T.T., Zhang, H., et al: ‘Event-triggered H∞ control for uncertain networked T–S fuzzy systems with time delay’, Neurocomputing, 2015, 157, pp. 273–279.
    2. 2)
      • 32. Nguyen, M.C., Trinh, H., Nam, P.T.: ‘Linear functional observers with guaranteed ε-convergence for discrete time-delay systems with input/output disturbances’, Int. J. Syst. Sci., 2016, 47, (13), pp. 3193–3205.
    3. 3)
      • 27. Wang, Y.L., Lim, C.C., Shi, P.: ‘Adaptively adjusted event-triggering mechanism on fault detection for networked control systems’, IEEE Trans. Cybern., 2017, 47, (8), pp. 2299–2311.
    4. 4)
      • 31. Nam, P.T., Pathirana, P.N., Trinh, H.: ‘ε-bounded state estimation for time-delay systems with bounded disturbances’, Int. J. Control, 2014, 87, (9), pp. 1747–1756.
    5. 5)
      • 9. Cassandras, C.G.: ‘The event-driven paradigm for control, communication and optimization’, J. Control Decis., 2014, 1, (1), pp. 3–7.
    6. 6)
      • 4. Gao, H.J., Li, X.W.: ‘H∞ filtering for discrete-time state-delayed systems with finite frequency specifications’, IEEE Trans. Autom. Control, 2011, 56, (12), pp. 2935–2941.
    7. 7)
      • 5. Zhuang, G.M., Li, Y.M., Li, Z.: ‘Fault detection for a class of uncertain nonlinear Markovian jump stochastic systems with mode-dependent time delays and sensor saturation’, Int. J. Syst. Sci., 2016, 47, (7), pp. 1514–1532.
    8. 8)
      • 22. Wang, Y.L., Shi, P., Lim, C.C., et al: ‘Event-triggered fault detection filter design for a continuous-time networked control system’, IEEE Trans. Cybern., 2016, 46, (12), pp. 3414–3426.
    9. 9)
      • 11. Zhang, X.M., Han, Q.L.: ‘Event-based H∞ filtering for sampled-data systems’, Bul. Inst. Politeh. ‘Gheorghe Gheorghiu-Dej’ Bucur. Ser. Autom., 2015, 51, pp. 55–69.
    10. 10)
      • 26. Liu, J.L., Yue, D.: ‘Event-based fault detection for networked systems with communication delay and nonlinear perturbation’, J. Franklin Inst., 2013, 350, (9), pp. 2791–2807.
    11. 11)
      • 23. Huong, D.C., Trinh, H., Tran, H.M., et al: ‘Approach to fault detection of time-delay systems using functional observers’, Electron. Lett., 2014, 50, (16), pp. 1132–1134.
    12. 12)
      • 34. Boyd, S., Ghaoui, L.E., Feron, E., et al.: ‘Linear matrix inequalities in system and control theory’ (SIAM, Philadelphia, PA, USA, 1994).
    13. 13)
      • 35. Wang, H., Yang, G.H.: ‘A finite frequency domain approach to fault detection for linear discrete-time systems’, Int. J. Control, 2008, 81, (7), pp. 1162–1171.
    14. 14)
      • 21. Zhang, D.W., Han, Q.L., Jia, X.C.: ‘Network-based output tracking control for T–S fuzzy systems using an event-triggered communication scheme’, Fuzzy Sets Syst., 2015, 273, pp. 26–48.
    15. 15)
      • 19. Jia, X.C., Chi, X.B., Han, Q.L., et al: ‘Event-triggered fuzzy H∞ control for a class of nonlinear networked control systems using the deviation bounds of asynchronous normalized membership functions’, Inf. Sci., 2014, 259, pp. 100–117.
    16. 16)
      • 17. Pan, Y.N., Yang, G.H.: ‘Event-triggered fuzzy control for nonlinear networked control systems’, Fuzzy Sets Syst., doi: 10.1016/j.fss.2017.05.010.
    17. 17)
      • 8. Li, H.Y., Gao, Y.B., Shi, P., et al: ‘Observer-based fault detection for nonlinear systems with sensor fault and limited communication capacity’, IEEE Trans. Autom. Control, 2016, 61, (9), pp. 2745–2751.
    18. 18)
      • 14. Ge, X.H., Han, Q.L.: ‘Distributed event-triggered H∞ filtering over sensor networks with communication delays’, Int. J. Comput. Inf. Sci., 2015, 291, pp. 128–142.
    19. 19)
      • 6. Dong, H.L., Wang, Z.D., Lam, J., et al: ‘Fuzzy-model-based robust fault detection with stochastic mixed time delays and successive packet dropouts’, IEEE Trans. Syst. Man Cybern. B (Cybern.), 2012, 42, (2), pp. 365–376.
    20. 20)
      • 36. Wei, Q.L., Liu, D.R., Yang, X.: ‘Infinite horizon self-learning optimal control of nonaffine discrete-time nonlinear systems’, IEEE Trans. Neural Netw. Learn. Syst., 2015, 26, (4), pp. 866–879.
    21. 21)
      • 24. Li, H.Y., Chen, Z., Wu, L.G., et al.: ‘Event-triggered fault detection of nonlinear networked systems’, IEEE Trans. Cybern., 2017, 47, (4), pp. 1041–1052.
    22. 22)
      • 12. Zhang, J., Peng, C.: ‘Event-triggered H∞ filtering for networked Takagi–Sugeno fuzzy systems with asynchronous constraints’, IET Signal Process., 2015, 9, (4), pp. 403–411.
    23. 23)
      • 10. Lehmann, D., Lunze, J.: ‘Extension and experimental evaluation of an event-based state-feedback approach’, Int. J. Robust Nonlinear Control, 2011, 19, (2), pp. 101–112.
    24. 24)
      • 30. Fu, M.Y., Xie, L.H.: ‘The sector bound approach to quantized feedback control’, IEEE Trans. Autom. control, 2005, 50, (11), pp. 1698–1711.
    25. 25)
      • 1. Yang, G.H., Wang, H.M.: ‘Fault detection for a class of uncertain state feedback control systems’, IEEE Trans. Control Syst. Technol., 2010, 18, (1), pp. 201–212.
    26. 26)
      • 29. Frezzatto, L., de Oliveira, M.C.C., Oliveira, R.C., et al: ‘Robust H∞ filter design with past output measurements for uncertain discrete-time systems’, Bul. Inst. Politeh. ‘Gheorghe Gheorghiu-Dej’ Bucur. Ser. Autom., 2016, 71, pp. 151–158.
    27. 27)
      • 33. Wang, Z., Yang, F., Ho, D., et al: ‘Robust H∞ filtering for stochastic time-delay systems with missing measurements’, IEEE Trans. Signal Process., 2006, 54, (7), pp. 2579–2587.
    28. 28)
      • 18. Li, Y.X., Yang, G.H.: ‘Model-based adaptive event-triggered control of strict-feedback nonlinear systems’, IEEE Trans. Neural Netw. Learn. Syst., doi: 10.1109/TNNLS.2017.2650238.
    29. 29)
      • 13. Wang, H.J., Shi, P., Zhang, J.H.: ‘Event-triggered fuzzy filtering for a class of nonlinear networked control systems’, Analog Integr. Circuits Signal Process., 2015, 113, pp. 159–168.
    30. 30)
      • 25. Hajshirmohamadi, S., Davoodi, M., Meskin, N., et al: ‘Event-triggered fault detection and isolation for discrete-time linear systems’, IET Control Theory Appl., 2016, 10, (5), pp. 526–533.
    31. 31)
      • 15. Zhang, J.H., Feng, G.: ‘Event-driven observer-based output feedback control for linear systems’, Automatica, 2014, 50, pp. 1852–1859.
    32. 32)
      • 2. Zhou, M.Y., Ding, S.X., Ding, E.L.: ‘Optimal fault detection for linear discrete time-varying systems’, Bul. Inst. Politeh. ‘Gheorghe Gheorghiu-Dej’ Bucur. Ser. Autom., 2010, 46, (8), pp. 1395–1400.
    33. 33)
      • 3. Dong, H.L., Wang, Z.D., Gao, H.J.: ‘Fault detection for Markovian jump systems with sensor saturations and randomly varying nonlinearities’, IEEE Trans. Circuits Syst. I: Reg. Pap., 2012, 59, (10), pp. 2354–2362.
    34. 34)
      • 7. Li, X.J., Yang, G.H.: ‘Fault detection and isolation for uncertain closed-loop systems based on adaptive and switching approaches’, Int. J. Robust Nonlinear Control, 2016, 26, pp. 2916–2937.
    35. 35)
      • 28. Zhang, Z.H., Yang, G.H.: ‘Event-triggered fault detection for a class of discrete-time linear systems using interval observers’, ISA Trans., 2017, 68, pp. 160–169.
    36. 36)
      • 16. Zhang, X.M., Han, Q.L.: ‘Event-triggered dynamic output feedback control for networked control systems’, IET Control Theory Appl., 2014, 8, (4), pp. 226–234.
    37. 37)
      • 37. Dong, H.L., Bu, X.Y., Hou, N., et al: ‘Event-triggered distributed state estimation for a class of time-varying systems over sensor networks with redundant channels’, Inf. Fusion, 2017, 36, pp. 243–250.
    38. 38)
      • 38. Dong, H.L., Wang, Z.D., Shen, B., et al.: ‘Variance-constrained H∞ control for a class of nonlinear stochastic discrete time-varying systems: the event-triggered design’, Automatica, 2016, 72, pp. 28–36.
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