access icon free Intelligent digital redesign for non-linear systems: observer-based sampled-data fuzzy control approach

In this study, an intelligent digital redesign (IDR) technique is proposed for an observer-based sampled-data fuzzy controller of non-linear systems. By using a Takagi–Sugeno fuzzy model, the pre-designed analog and sampled-data fuzzy controllers are supposed, and these discretised closed-loop systems are obtained, respectively. Based on the IDR problem, the authors guarantee both stability and state-matching conditions. Unlike the previous techniques, the proposed IDR not only improves the state-matching performance using the state-matching error cost function, but is also derived in the strict linear matrix inequality format. In a numerical example, the effectiveness of the proposed technique and the results of the improved performance are shown.

Inspec keywords: control system synthesis; fuzzy control; observers; intelligent control; linear matrix inequalities; stability; sampled data systems; discrete systems; closed loop systems; nonlinear control systems

Other keywords: state matching error cost function; state matching condition; nonlinear system; linear matrix inequality; Takagi-Sugeno fuzzy model; intelligent digital redesign; stability; IDR problem; observer-based sampled data fuzzy control approach; discretised closed loop system

Subjects: Linear algebra (numerical analysis); Discrete control systems; Fuzzy control; Nonlinear control systems; Control system analysis and synthesis methods; Stability in control theory

References

    1. 1)
    2. 2)
    3. 3)
    4. 4)
    5. 5)
    6. 6)
    7. 7)
    8. 8)
    9. 9)
    10. 10)
    11. 11)
    12. 12)
      • 14. Chen, T., Francis, B.: ‘Optimal sampled-data control systems’ (Springer, London, 1995).
    13. 13)
    14. 14)
    15. 15)
    16. 16)
      • 30. ‘SCILAB Software’: http://www.scilab.org/.
    17. 17)
    18. 18)
    19. 19)
      • 32. Kim, D.W., Lee, H.J., Tomizuka, M.: ‘Fuzzy stabilization of nonlinear systems under sampled-data feedback: an exact discrete-time model approach’, IEEE Trans. Fuzzy Syst., 2010, 18, (2), pp. 251260.
    20. 20)
    21. 21)
    22. 22)
    23. 23)
      • 17. Chang, W., Park, J.B., Lee, H.J., et al : ‘LMI approach to digital redesign of linear time-invariant systems’, IEE Proc. Control Theory Appl., 2002, 149, 297–302.
    24. 24)
    25. 25)
    26. 26)
    27. 27)
    28. 28)
    29. 29)
    30. 30)
    31. 31)
      • 6. Tanaka, K., Wand, H.O.: ‘Fuzzy control systems design and analysis: a linear matrix inequality approach’ (Wiley, New York, 2001).
    32. 32)
    33. 33)
    34. 34)
    35. 35)
    36. 36)
    37. 37)
    38. 38)
    39. 39)
    40. 40)
      • 40. Boulkroune, A., Tadjine, M., M'Saad, M., et al : ‘Adaptive fuzzy observer for uncertain nonliear systems’, Control Intell. Syst., 2011, 39, (3), pp. 145150.
    41. 41)
    42. 42)
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-cta.2015.0244
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