Skip to main content
SpringerLink
Log in

Adaptive fuzzy control for field-oriented induction motor drives

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

With the field-oriented method, the dynamic behavior of the induction motor is rather similar to that of a separately excited DC motor. However, the control performance of the induction motor is still influenced by the unmodelled dynamics or external disturbances, and to compensate for these uncertainties, adaptive fuzzy control is proposed. The overall control signal consists of two elements, (1) the equivalent control which is used for linearization of the induction motor’s model through feedback of the state vector. The equivalent control includes neurofuzzy approximators of the unknown parts of the induction motor model (2) the supervisory control which consists of an \(H_{\infty}\) term and compensates for parametric uncertainties of the induction motor model and external disturbances. The performance of the proposed adaptive fuzzy \(H_{\infty}\) controller is compared to backstepping nonlinear control through simulation tests.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

Similar content being viewed by others

References

  1. Holtz J (2002) Sensorless control of induction motor drives. Proc IEEE 90(8):1359–1394

    Article  Google Scholar 

  2. Bodson M, Chiasson J, Novotnak R (1994) High-performance induction motor control via input-output linearization. IEEE Control Syst Mag 24–33

  3. Georges D, De Wit C, Ramirez J (1999) Nonlinear H 2 and \(H_\infty\) optimal controllers for current-fed induction motors. IEEE Trans Automat Control 44(7):1430–1435

    Article  MathSciNet  MATH  Google Scholar 

  4. Marino R, Peresada S, Valigi P (1993) Adaptive input-output linearizing control of induction motors. IEEE Trans Automat Control 38(2):208–221

    Article  MathSciNet  MATH  Google Scholar 

  5. Marino R, Peresada S, Tomei P (1999) Global adaptive output feedback control of induction motors with uncertain rotor resistance. IEEE Trans Automat Control 44(5):967–983

    Article  MathSciNet  MATH  Google Scholar 

  6. Chiasson J (1998) A new approach to dynamic feedback linearization control of an induction motor. IEEE Trans Automat Control 43(3):391–397

    Article  MathSciNet  MATH  Google Scholar 

  7. Rigatos GG (2009) Adaptive fuzzy control of DC motors using state and output feedback. Electric Power Syst Res 79(11):1579–1592 (Elsevier)

    Google Scholar 

  8. Lin FJ, Wai RJ, Lin CH, Liu DC (2000) Decoupled stator-flux-oriented induction motor drive with fuzzy neural network uncertainty observer. IEEE Trans Ind Electron 47(2):356–367

    Article  Google Scholar 

  9. Nounou HN, Rehman H (2007) Application of adaptive fuzzy control to AC machines. Appl Soft Comput 7(3):899–907 (Elsevier)

    Article  Google Scholar 

  10. Wai RJ, Chang JM (2003) Implementation of robust wavelet-neural-network sliding-mode control for induction servo motor drive. IEEE Trans Ind Electron 50(6):1317–1334

    Article  Google Scholar 

  11. Wai RJ, Chang HH (2004) Backstepping wavelet neural network control for indirect field-oriented induction motor drive. IEEE Trans Neural Netw 15(2):367–382

    Article  Google Scholar 

  12. Brdys MA, Kulowski GJ (1999) Dynamic neural controllers for induction motor. IEEE Trans Neural Netw 10(2):340–355

    Article  Google Scholar 

  13. Rigatos GG, Tzafestas SG (2007) \(H_{\infty}\) tracking for uncertain SISO nonlinear systems: an observer-based adaptive fuzzy approach. Int J Syst Sci 38(6):459–472 (Taylor and Francis)

    Article  MathSciNet  MATH  Google Scholar 

  14. Rigatos GG, Tzafestas SG (2006) Adaptive fuzzy control for the ship steering problem. J Mechatron 16(6):479–489 (Elsevier)

    Google Scholar 

  15. Leu Y-G, Lee T-T, Wang W-Y (1999) Observer-based adaptive fuzzy-neural control for unknown nonlinear dynamical systems. IEEE Trans Syst Man Cybern Part B Cybern 29:583–591

    Article  Google Scholar 

  16. Ge SS, Hang CC, Zhang T (1999) Adaptive neural network control of nonlinear systems by state and output feedback. IEEE Trans Syst Man Cybern Part B Cybern 29:818–828

    Article  Google Scholar 

  17. Tong S, Li H-X, Chen G (2004) Adaptive fuzzy decentralized control for a class of large-scale nonlinear systems. IEEE Trans Syst Man Cybern Part B Cybern 34:770–775

    Article  Google Scholar 

  18. Chen B-S, Lee C-H, Chang Y-C (1996) “\(H_{\infty}\) tracking design of uncertain nonlinear SISO systems: adaptive fuzzy approach. IEEE Trans Fuzzy Syst 4:32–43

    Article  Google Scholar 

  19. Lublin L, Athans M (1995) An experimental comparison of and designs for interferometer testbed. In: Francis B, Tannenbaum A (eds) Lectures notes in control and information sciences: feedback control, nonlinear systems and complexity, Springer, pp 150–172

  20. Kurylowicz A, Jaworska I, Tzafestas SG (1993) Robust stabilizing control: an overview. In: Tzafestas SG (eds) Applied control: current trends and modern methodologies. Marcel Dekker, New York, pp 289–324

    Google Scholar 

  21. Doyle JC, Glover K, Khargonekar PP, Francis BA (1989) State-space solutions to standard H 2 and \(H_{\infty}\) control problems. IEEE Trans Automat Control 34:831–847

    Article  MathSciNet  MATH  Google Scholar 

  22. Rigatos GG, Tzafestas CS, Tzafestas SG (2000) Mobile robot motion control in partially unknown environments using a sliding-mode fuzzy-logic controller. Rob Auton Syst 33:1–11 (Elsevier)

    Google Scholar 

  23. Rigatos GG (2003) Fuzzy stochastic automata for intelligent vehicle control. IEEE Trans Ind Electron 50:76–79

    Article  Google Scholar 

  24. Wang LX (1994) Adaptive fuzzy systems and control: design and stability analysis. Prentice Hall, Englewood Cliffs

    Google Scholar 

  25. Wang LX (1998) A course in fuzzy systems and control. Prentice Hall, Englewood Cliffs

    Google Scholar 

  26. Su CY, Stephanenko Y (1994) Adaptive fuzzy control of a class of nonlinear systems. IEEE Trans Fuzzy Syst 2:285–294

    Article  Google Scholar 

  27. Spooner JT, Passino KM (1996) Stable adaptive control using fuzzy systems and neural networks. IEEE Trans Fuzzy Syst 4:339–359

    Article  Google Scholar 

  28. Horng JH (1999) Neural adaptive tracking control of a DC motor. Inf Sci 118:1–13 (Elsevier)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Unit of Industrial Automation, Industrial Systems Institute, 26504, Rion Patras, Greece

    Gerasimos G. Rigatos

Authors
  1. Gerasimos G. Rigatos
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Gerasimos G. Rigatos.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Rigatos, G.G. Adaptive fuzzy control for field-oriented induction motor drives. Neural Comput & Applic 21, 9–23 (2012). https://doi.org/10.1007/s00521-011-0645-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-011-0645-z

Keywords

Search

Navigation