Neural Dynamic Surface Control for Three-Phase PWM Voltage Source Rectifier

  • Liang DiaoEmail author
  • Dan Wang
  • Zhouhua Peng
  • Lei Guo
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9377)


In this brief, a neural dynamic surface control algorithm is proposed for three-phase pulse width modulation voltage source rectifier with the parametric variations. Neural networks are employed to approximate the uncertainties, including the parametric variations and the unknown load-resistance. The actual control laws are derived by using the dynamic surface control method. Furthermore, a linear tracking differentiator is introduced to replace the first-order filter to calculate the derivative of the virtual control law. Thus, the peaking phenomenon of the filter is suppressed during the initial phase. The system stability is analyzed by using the Lyapunov theory. Simulation results are provided to validate the efficacy of the proposed controller.


PWM rectifier dynamic surface control neural network linear tracking differentiator 


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  1. 1.
    Choi, D.K., Lee, K.B.: Dynamic performance improvement of AC/DC converter using model preictive direct power control with finite control set. IEEE Transactions on Industrial Electronics 62(2), 757–767 (2015)CrossRefGoogle Scholar
  2. 2.
    Wu, R., Dewan, S.B., Slemon, G.R.: Analysis of an ac to dc voltage source converter using PWM with phase and amplitude control. IEEE Transactions on Industry Applications 27(2), 355–364 (1991)CrossRefGoogle Scholar
  3. 3.
    Sato, A., Noguchi, T.: Voltage-source PWM rectifier-inverter based on direct power control and its operation characteristic. IEEE Transactions on Power Electronics 26(5), 1559–1567 (2011)CrossRefGoogle Scholar
  4. 4.
    Xia, C.L., Wang, M., Song, Z.F., Liu, T.: Robust model predictive current control of three-phase voltage source PWM rectifier with online disturbance observation. IEEE Transactions on Industrial Informatics 8(3), 459–471 (2012)CrossRefGoogle Scholar
  5. 5.
    Liang, J.Q., Qiao, W., Hareley, R.G.: Feed-forward transient current control for low-voltage ride-through enhancement of DFIG wind turbines. IEEE Transactions on Energy Conversion 25(3), 836–843 (2010)CrossRefGoogle Scholar
  6. 6.
    Yin, Z.G., Liu, J., Zhong, Y.R.: Study and control of three-phase PWM rectifier based on dual single-input single-output model. IEEE Transactions on Industrial Informatics 9(2), 1064–1073 (2013)CrossRefGoogle Scholar
  7. 7.
    Allag, A., Hammoudi, M.Y., Mimoune, S.M., Ayad, M.Y.: Adaptive backstepping voltage controller design for an PWM AC-DC converter. International Journal of Electrical and Power Engineering 1(1), 62–69 (2007)Google Scholar
  8. 8.
    Wang, G.D., Wai, R.J., Liao, Y.: Design of backstepping power control for grid-side converter of voltage source converter-based high-voltage dc wind power generation system. IET Renewable Power Generation 7(2), 118–133 (2013)CrossRefGoogle Scholar
  9. 9.
    Swaroop, D., Gerdes, J.C., Yip, P.P., Hedrick, J.K.: Dynamic surface control of nonlinear systems. In: Proceedings of the 1997 American Control Conference, Albuquerque, pp. 3028–3034 (1997)Google Scholar
  10. 10.
    Wang, D., Huang, J.: Neural network-based adaptive dynamic surface control for a class of uncertain nonlinear systems in strict-feedback form. IEEE Transactions on Neural Networks 16(1), 195–202 (2005)CrossRefGoogle Scholar
  11. 11.
    Han, S.I., Lee, J.M.: Precise positioning of nonsmooth dynamic systems using fuzzy wavelet echo state network and dynamic surface sliding mode control. IEEE Transactions on Industrial Electronics 60(11), 5124–5136 (2013)CrossRefGoogle Scholar
  12. 12.
    Guo, B.Z., Han, J.Q., Xi, F.B.: Linear tracking-differentiator and application to online estimation of the frequency of a sinusoidal signal with random noise perturbation. International Journal of Systems Science 33(5), 351–358 (2002)MathSciNetCrossRefGoogle Scholar

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© Springer International Publishing Switzerland 2015

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Authors and Affiliations

  1. 1.School of Marine EngineeringDalian Maritime UniversityDalianPR China

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