Springer Nature is making Coronavirus research free. View research | View latest news | Sign up for updates

Takagi–Sugeno fuzzy-based integral sliding mode control for wind energy conversion systems with disturbance observer

  • 15 Accesses


The wind energy conversion system (WECS) is a system which allows the conversion of wind-generated kinetic energy to electrical energy. One of the currently used systems to generate electrical energy is the permanent magnet synchronous generator. The proposed control method in this paper is Takagi–Sugeno (T–S) fuzzy model-based integral sliding mode control (ISMC). The ISMC method performs robustness to external disturbances and noises. The one important issue of the WECS is the variable wind speed which varies drastically over short periods of time. The solution to the problem of the wind speed estimation is the disturbance observer application which was also used to estimate the wind speed. The use of T–S fuzzy model is justified by the robustness to unmatched disturbances. The novelty of the proposed combination of control techniques is the solution to problems of nonlinearity, estimation of the aerodynamic torque and the chattering natural for sliding mode control. The simulation was conducted in MATLAB/Simulink software. The results are provided in the result-related section.

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

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


  1. 1.

    Jha AR (2011) Wind turbine technology. CRC Press, Boca Raton

  2. 2.

    Kolar JW, Friedli T, Krismer F et al (2011) Conceptualization and multi-objective optimization of the electric system of an airborne wind turbine. In: IEEE international symposium on industrial electronics

  3. 3.

    Gawande SP, Porate KB (2009) Review of parallel operation of synchronous generator and induction generator for stability. In: Second international conference on emerging trends in engineering and technology

  4. 4.

    Wu B, Lang Y, Zargari N, Kouro S (2011) Power conversion and control of wind energy systems. Wiley, Hoboken

  5. 5.

    Kurronen P, Haavisto M, Pyrhonen J (2010) Challenges in applying permanent magnet (PM) technology to wind power generators. In: European wind energy conference

  6. 6.

    Rodrigues J, Cortes P (2012) Predictive control of power converters electrical drives. Wiley, Hoboken

  7. 7.

    Shen YX, He QN, Pan TL et al (2013) T–S fuzzy robust fault-tolerant control strategy for wind energy conversion system. In: 8th conference on industrial electronics and applications

  8. 8.

    Kaewpraek N, Assawinchaichote W (2013) Control of PMSG wind energy conversion system with T–S fuzzy state-feedback controller. Appl Mech Mater 446–447(2014):728–732

  9. 9.

    Ounnas D, Ramdani M, Chenikher S et al (2016) Optimal reference model based fuzzy tracking control for wind energy conversion system. Int J Renew Energy Res 6(3):1129–1136

  10. 10.

    Muhando BE, Senjyu T, Urasaki N (2007) Gain scheduling control of variable speed WTG under widely varying turbulence loading. Renew Energy 32(14):2407–2423

  11. 11.

    Sumbekov S, Do TD (2018) Sliding mode controller with DOBC and MTPA Trajectroty for surface-mounted PMSM. In: 4th international conference on green technology and sustainable development, Ho Chi Minh City, Vietnam

  12. 12.

    Busca C, Stan AI, Stanciu T et al (2010) Control of permanent magnet synchronous generator for large wind turbines. In: International symposium on industrial electronics

  13. 13.

    Rahim AHMA, Khan MH (2013) An adaptive optimum SMES controller for a PMSG wind generation system. In: IEEE power and energy society general meeting

  14. 14.

    Mozayan SM, Saad M, Vahedi H et al (2016) Sliding mode control of PMSG wind turbine based on enhanced exponential reaching law. IEEE Trans on Ind Electron 63(10):6148–6159

  15. 15.

    Yao B, Tomizuka M (1997) Adaptive robust control of SISO nonlinear systems in a semi-strict feedback form. Automatica 33(5):893–900

  16. 16.

    Rubio JJ (2018) Robust feedback linearization for nonlinear processes control. ISA Trans 74:155–164

  17. 17.

    Rubio JJ, Pieper J, Meda-Campana JA et al (2018) Modelling and regulation of two mechanical systems. IET Sci Meas Technol 12(5):657–665

  18. 18.

    Sun Y, Qiang H, Mei X et al (2018) Modified repetitive learning control with unidirectional control input for uncertain nonlinear systems. Neural Comput Appl 30(6):2003–2012

  19. 19.

    Rubio JJ (2016) Structure control for the disturbance rejection in two electromechanical processes. J Franklin Inst 353(4):3610–3631

  20. 20.

    Lin CH (2013) Novel modified elman neural network control for PMSG system based on wind turbine emulator. Math Prob Eng 2013:1–15

  21. 21.

    Nagrath IJ, Gopal M (2008) Control systems engineering, 5th edn. Anshan, Tunbridge Wells

  22. 22.

    Lahfaoui B, Zouggar S, Mohammed B et al (2017) Real time study of PnO MPPT control for small wind PMSG turbine systems using arduino microcontroller. Energy Procedia 111:1000–1009

  23. 23.

    Abdullah MA, Yatim AHM, Tan CW et al (2012) A review of maximum power point tracking algorithms for wind energy systems. Renew Sustain Energy Rev 16(5):3220–3227

  24. 24.

    Ayadi M, Naifar O, Derbel N (2017) Sensorless control with an adaptive sliding mode observer for wind PMSG systems. In: 14th international multi-conference on systems, signals and devices

  25. 25.

    Orlando NA, Liserre M, Mastromauro RA et al (2013) A survey of control issues in PMSG-based small wind-turbine systems. IEEE Trans Ind Inform 9(3):1211–1221

  26. 26.

    Le AV, Do TD (2018) High-order observers-based LQ control scheme for wind speed and uncertainties estimation in WECSs. Optim Control Appl Methods 39(5):1818–1832

  27. 27.

    Gauterin E, Kammerer P, Kühn M et al (2016) Effective wind speed estimation: comparison between Kalman filter and Takagi–Sugeno observer techniques. ISA Trans 62:60–72

  28. 28.

    Hodzic M, Tai LC (2016) Grey predictor reference model for assisting particle swarm optimization for wind turbine control. Renew Energy 86:251–256

  29. 29.

    Song D, Yang J, Dong M, Joo YH (2017) Kalman filter-based wind speed estimation for wind turbine control. Int J Control Autom Syst 15(3):1089–1096

  30. 30.

    Kaur A, Kaur A (2012) Comparison of Mamdani-type and Sugeno-type fuzzy inference systems for air conditioning system. Int J Soft Comput Eng 2(2):323–325

  31. 31.

    Do TD, Kwak S, Choi HH et al (2014) Suboptimal control scheme design for interior permanent-magnet synchronous motors: an SDRE-based approach. IEEE Trans on Power Electron 29(6):3021

  32. 32.

    Vu NTT, Yu DY, Choi HH et al (2013) T–S fuzzy-model-based sliding-mode control for surface-mounted permanent-magnet synchronous motors considering uncertainties. IEEE Trans Ind Electron 60(10):4281–4291

  33. 33.

    Choi HH, Vu NTT, Jung JW (2012) Design and implementation of a Takagi–Sugeno fuzzy speed regulator for a permanent magnet synchronous motor. IEEE Trans Ind Electron 59(8):3069–3077

  34. 34.

    Fridman L, Poznyak AS, Bejarano FJ (2014) Robust ouput LQ optimal control via integral sliding modes. Springer, Berlin

  35. 35.

    Riedinger P, Kratz F, Iung C et al (2002) Linear quadratic optimization for hybrid systems. In: 38th conference on decision and control, Phoenix, Arizona

  36. 36.

    Martinez F, Herrero LH, de Pablo S (2014) Open loop wind turbine emulator. Renew Energy 63(2014):212–221

  37. 37.

    Popiolek J (1989) Some properties of functions modul and signum. Formal Math 1(2):263–264

  38. 38.

    Kim EK, Mwasilu F, Choi HH et al (2015) An observer-based optimal voltage control scheme for three-phase UPS systems. IEEE Trans Ind Electron 62(4):2073–2081

  39. 39.

    Li S, Yang J, Chen W, Chen X (2014) Disturbance observer based control. Taylor and Francis Group, Boca Raton

  40. 40.

    Radke A, Gao Z (2006) A survey of state and disturbance observers for practitioners. In: American control conference. Minneapolis, USA

  41. 41.

    Valenciaga F, Puleston PF (2008) High-order sliding control for a wind energy conversion system based on a permanent magnet synchronous generator. IEEE Trans Energy Conv 23:860–867

Download references


The work was sponsored by the Ministry of Education and Science of the Republic of Kazakhstan, Grant/Award Numbers: BR0523652 and BR05236524.

Author information

Correspondence to Ton Duc Do.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Sumbekov, S., Phuc, B.D.H. & Do, T.D. Takagi–Sugeno fuzzy-based integral sliding mode control for wind energy conversion systems with disturbance observer. Electr Eng (2020).

Download citation


  • Wind energy conversion system
  • Permanent magnet synchronous generator
  • Takagi–Sugeno fuzzy model
  • Integral sliding mode control
  • Disturbance observer