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Observer-based adaptive fuzzy dynamic surface control of non-linear non-strict feedback system

Observer-based adaptive fuzzy dynamic surface control of non-linear non-strict feedback system

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This study develops a scheme of an adaptive fuzzy dynamic surface control (DSC) for a non-linear non-strict feedback system with immeasurable states. By utilising the universal approximation property of a fuzzy logic system, a state observer is designed to estimate the unmeasured states. Based on the back-stepping DSC design technique, an adaptive fuzzy output feedback control design method is developed. The proposed method can overcome the ‘explosion of complexity’ problem, and also can eliminate the limitation of the measurable states. It is proved that the control method can guarantee that all the signals in the closed-loop system are bounded and the system output can track the given reference signal well. The simulation results are provided to demonstrate the effectiveness of the proposed control method.

References

    1. 1)
      • 1. Wang, L.X.: ‘Stable adaptive fuzzy control of nonlinear systems’, IEEE Trans. Fuzzy Syst., 1993, 1, pp. 146155.
    2. 2)
      • 2. Tong, S.C., Li, H.X.: ‘Fuzzy adaptive sliding-mode control for MIMO nonlinear systems’, IEEE Trans. Fuzzy Syst., 2003, 11, pp. 354360.
    3. 3)
      • 3. Leu, Y.G., Wang, W.Y., Lee, T.T.: ‘Observer-based direct adaptive fuzzy-neural control for nonaffine nonlinear systems’, IEEE Trans. Neural Netw., 2005, 16, pp. 853861.
    4. 4)
      • 4. Labiod, S., Guerra, T.M.: ‘Adaptive fuzzy control of a class of SISO nonaffine nonlinear systems’, Fuzzy Sets Syst., 2007, 158, pp. 11261137.
    5. 5)
      • 5. Boulkroune, A., M'Saad, M., Farza, M.: ‘Fuzzy approximation based indirect adaptive controller for multi-input multi-output non-affine systems with unknown control direction’, IET Control Theory Appl., 2012, 6, pp. 26192629.
    6. 6)
      • 6. Polycarpou, M.M., Mears, M.J.: ‘Stable adaptive tracking of uncertain systems using nonlinearly parametrized on-line approximators’, Int. J. Control, 1998, 70, pp. 363384.
    7. 7)
      • 7. Chen, M., Ge, S.S., Ren, B.B.: ‘Adaptive tracking control of uncertain MIMO nonlinear systems with input constraints’, Automatica, 2011, 47, pp. 452465.
    8. 8)
      • 8. Zhao, F., Ge, S.S., Tu, F.W., et al: ‘Adaptive neural network control for active suspension system with actuator saturation’, IET Control Theory Appl., 2016, 10, pp. 16961705.
    9. 9)
      • 9. Tong, S.C., Li, Y.M.: ‘Observer-based fuzzy adaptive control for strict-feedback nonlinear systems’, Fuzzy Sets Syst., 2009, 160, pp. 17491764.
    10. 10)
      • 10. Chen, W.S.: ‘Adaptive backstepping dynamic surface control for systems with periodic disturbances using neural networks’, IET Control Theory Appl., 2009, 3, pp. 13831394.
    11. 11)
      • 11. Choi, Y.H., Sung, J.Y.: ‘Simple adaptive output-feedback control of non-linear strict-feedback time-delay systems’, IET Control Theory Appl., 2016, 10, pp. 5866.
    12. 12)
      • 12. Long, L.J., Zhao, J.: ‘Adaptive fuzzy output-feedback control for switched uncertain non-linear systems’, IET Control Theory Appl., 2016, 10, pp. 752761.
    13. 13)
      • 13. Jagannathan, S., He, P.: ‘Neural-network-based state feedback control of a nonlinear discrete-time system in non-strict feedback form’, IEEE Trans. Neural Netw., 2008, 19, pp. 20732087.
    14. 14)
      • 14. Chen, B., Liu, X.P., Ge, S.S., et al: ‘Adaptive fuzzy control of a class of nonlinear systems by fuzzy approximation approach’, IEEE Trans. Fuzzy Syst., 2012, 20, pp. 10121021.
    15. 15)
      • 15. Chen, B., Lin, C., Liu, X.P., et al: ‘Adaptive fuzzy tracking control for a class of MIMO nonlinear systems in nonstrict-feedback form’, IEEE Trans. Cybern., 2015, 45, pp. 27442755.
    16. 16)
      • 16. Wang, H.Q., Chen, B., Liu, K.F., et al: ‘Adaptive neural tracking control for a class of nonstrict-feedback stochastic nonlinear systems with unknown backlash-like hysteresis’, IEEE Trans. Neural Netw. Learn. Syst., 2014, 25, pp. 947958.
    17. 17)
      • 17. Zhou, Q., Wang, L.J., Wu, C.W., et al: ‘Adaptive fuzzy control for nonstrict-feedback systems with input saturation and output constraint’, IEEE Trans. Syst., Man, Cybern.: Syst., 2017, 47, pp. 112.
    18. 18)
      • 18. Wang, H.Q., Liu, K.F., Liu, X.P., et al: ‘Neural-based adaptive output-feedback control for a class of nonstrict-feedback stochastic nonlinear systems’, IEEE Trans. Cybern., 2015, 45, pp. 19771987.
    19. 19)
      • 19. Chen, B., Zhang, H.G., Lin, C.: ‘Observer-based adaptive neural network control for nonlinear systems in nonstrict-feedback form’, IEEE Trans. Neural Netw. Learn. Syst., 2016, 27, pp. 8998.
    20. 20)
      • 20. Li, Y.M., Tong, S.C.: ‘Adaptive fuzzy output-feedback stabilization control for a class of switched nonstrict-feedback nonlinear systems’, IEEE Trans. Cybern., 2017, 47, pp. 10071016.
    21. 21)
      • 21. Tong, S.C., Li, Y.M., Sui, S.: ‘Adaptive fuzzy tracking control design for uncertain non-strict feedback nonlinear systems’, IEEE Trans. Fuzzy Syst., 2016, 24, pp. 14411454.
    22. 22)
      • 22. Swaroop, S., Hedrick, J.K., Yip, P.P., et al: ‘Dynamic surface control for a class of nonlinear systems’, IEEE Trans. Autom. Control, 2000, 45, pp. 18931899.
    23. 23)
      • 23. Wang, D., Huang, J.: ‘Neural network-based adaptive dynamic surface control for a class of uncertain nonlinear systems in strict-feedback form’, IEEE Trans. Neural Netw., 2015, 16, pp. 195202.
    24. 24)
      • 24. Wang, M., Liu, X., Shi, P.: ‘Adaptive neural control of pure-feedback nonlinear time-delay systems via dynamic surface technique’, IEEE Trans. Syst., Man, Cybern., Part B :Cybern., 2011, 41, pp. 16811692.
    25. 25)
      • 25. Yu, Z.X., Yu, Z.S.: ‘Adaptive neural dynamic surface control for nonlinear pure-feedback systems with multiple time-varying delays: a Lyapunov–Razumikhin method’, Asian J. Control, 2013, 15, pp. 11241138.
    26. 26)
      • 26. Zhang, L.L., Hua, C.C., Guan, X.P.: ‘Distributed output feedback consensus tracking prescribed performance control for a class of non-linear multi-agent systems with unknown disturbances’, IET Control Theory Appl., 2016, 10, pp. 877883.
    27. 27)
      • 27. Zhai, D., An, L.W., Li, J.H., et al: ‘Simplified filtering-based adaptive fuzzy dynamic surface control approach for non-linear strict-feedback systems’, IET Control Theory Appl., 2016, 10, pp. 493503.
    28. 28)
      • 28. Sung, J.Y., Jin, B.P., Yoon, H.C.: ‘Adaptive dynamic surface for stabilization of parametric strict-feedback nonlinear systems with unknown time delays’, IEEE Trans. Autom. Control, 2007, 52, pp. 23602365.
    29. 29)
      • 29. Yan, X.H., Chen, M., Wu, Q.X., et al: ‘Dynamic surface control for a class of stochastic non-linear systems with input saturation’, IET Control Theory Appl., 2016, 10, pp. 3543.
    30. 30)
      • 30. Dawson, D.M., Carroll, J.J., Schneider, M.: ‘Integrator backstepping control of a brush DC motor turning a robotic load’, IEEE Trans. Control Syst. Tech., 1994, 2, pp. 233244.
    31. 31)
      • 31. Fridman, L., Shtessel, Y., Edwards, C., et al: ‘Higher-order sliding-mode observer for state estimation and input reconstruction in nonlinear systems’, Int. J. Robust Nonlinear Control, 2008, 18, pp. 399412.
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