Skip to main content

Advertisement

SpringerLink
Log in

Fuzzy dissipative and observer control for wind generator systems: a fuzzy time-dependent LKF approach

  • Original paper
  • Published:
Nonlinear Dynamics Aims and scope Submit manuscript

Abstract

This paper examines the network-based dissipative and fuzzy observer control for wind generator systems (WGSs) by using time-dependent fuzzy Lyapunov–Krasovskii functional (LKF) approach. The main aim of this work is to design the desired dissipative and observer control gains. An effective time-dependent fuzzy LKF is constructed, and using a new mathematical analysis method dissipative and fuzzy observer controller performance are designed for wind generator systems. The results are represented in the form of linear matrix inequalities. Takagi–Sugeno fuzzy model-based algorithm is considered to increase the efficiency of the system and guarantee almost convergence. To show the effectiveness of the proposed novel method, simulations for WGS are provided.

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

Similar content being viewed by others

References

  1. Harnefors, L.: Speed estimation from noisy resolver signals. In: Proceedings of 6th International Conference on Power Electron and Variable Speed Drives (1996)

  2. Bianchi, Fernando D., Mantz, Ricardo J., Christiansen, Carlos F.: Gain scheduling control of variable-speed wind energy conversion systems using quasi-LPV models. Control Eng. Pract. 13(2), 247–255 (2005)

    Article  Google Scholar 

  3. Deng, Y., Zhou, J.: LPV H-infinity controller design for a wind power generator. In: Proceedings of the IEEE Conference on Robotics, Automation and Mechatronics, Chengdu, China, 21–24 Sept, pp. 873–878 (2008)

  4. Bououden, S., Chadli, M., Filali, S., El Hajjaji, A.: Fuzzy model based multivariable predictive control of a variable speed wind turbine: LMI approach. Renew. Energy 37(1), 434–439 (2012)

    Article  Google Scholar 

  5. Thomsen, S.C., Poulsen, N.K.: A disturbance decoupling nonlinear control law for variable speed wind turbines. In: The 15th IEEE Mediterranean Conference on control and Automation MED’07, Athens, Greece. pp. 27–29 (2007)

  6. Sung, H.C., Park, J.B., Joo, Y.H.: Robust observer-based fuzzy control for variable speed wind power system: LMI approach. Int. J. Control Autom. Syst. 9(6), 1103–1110 (2011)

    Article  Google Scholar 

  7. Takagi, T., Sugeno, M.: Fuzzy identification of systems and its applications to modeling and control. IEEE Trans. Syst. Man Cybern. 1, 116–132 (1985)

    Article  MATH  Google Scholar 

  8. Lee, D.H., Joo, Y.H.: On the generalized local stability and local stabilization conditions for discrete-time Takagi–Sugeno fuzzy systems. IEEE Trans. Fuzzy Sys. 22(6), 1654–1668 (2014)

    Article  Google Scholar 

  9. Chak, Y.-C., Varatharajoo, R., Razoumny, Y.: Disturbance observer-based fuzzy control for flexible spacecraft combined attitude & sun tracking system. Acta Astronaut. 133, 302–310 (2017)

    Article  Google Scholar 

  10. Zhao, L., Gao, H., Karimi, H.R.: Robust stability and stabilization of uncertain T–S fuzzy systems with time-varying delay: an input–output approach. IEEE Trans. Fuzzy Syst. 21(5), 883–897 (2013)

    Article  Google Scholar 

  11. Li, H., Wu, C., Yin, S., Lam, H.-K.: Observer-based fuzzy control for nonlinear networked systems under unmeasurable premise variables. IEEE Trans. Fuzzy Syst. 24(5), 1233–1245 (2016)

    Article  Google Scholar 

  12. Koo, G.B., Park, J.B., Joo, Y.H.: Decentralized sampled-data fuzzy observer design for nonlinear interconnected systems. IEEE Trans. Fuzzy Syst. 24(3), 661–674 (2016)

    Article  Google Scholar 

  13. Wang, B., Zhang, D., Cheng, J., Park, J.H.: Fuzzy model-based nonfragile control of switched discrete-time systems. Nonlinear Dyn. 93(4), 2461–2471 (2018)

    Article  MATH  Google Scholar 

  14. Wen, S., Zeng, Z., Huang, T.: Robust \({H}_{\infty }\) output tracking control for fuzzy networked systems with stochastic sampling and multiplicative noise. Nonlinear Dyn. 70(2), 1061–1077 (2012)

    Article  MathSciNet  MATH  Google Scholar 

  15. Guan, Y., Han, Q.-L., Yao, H., Ge, X.: Robust event-triggered \({H}_{\infty }\) controller design for vehicle active suspension systems. Nonlinear Dyn. 94(1), 627–638 (2018)

    Article  MATH  Google Scholar 

  16. Yang, F., Zhang, H., Wang, Y.: An enhanced input-delay approach to sampled-data stabilization of T–S fuzzy systems via mixed convex combination. Nonlinear Dyn. 75(3), 501–512 (2014)

    Article  MathSciNet  MATH  Google Scholar 

  17. Zhao, X., Wang, X., Zong, G., Li, H.: Fuzzy-approximation-based adaptive output-feedback control for uncertain nonsmooth nonlinear systems. IEEE Trans. Fuzzy Syst. 26(6), 3847–3859 (2018)

    Article  Google Scholar 

  18. Zhao, X., Shi, P., Zheng, X.: Fuzzy adaptive control design and discretization for a class of nonlinear uncertain systems. IEEE Trans. Cybern. 46(6), 1476–1483 (2016)

    Article  Google Scholar 

  19. Chang, X.-H., Wang, Y.-M.: Peak-to-peak filtering for networked nonlinear DC motor systems with quantization. IEEE Trans. Ind. Inform. 14(12), 5378–5388 (2018)

    Article  Google Scholar 

  20. Chang, X.-H., Yang, G.-H.: Nonfragile \( {H_\infty } \) filtering of continuous-time fuzzy systems. IEEE Trans. Signal Process. 59(4), 1528–1538 (2011)

    Article  MathSciNet  MATH  Google Scholar 

  21. Zhang, D., Shi, P., Li, Y.: Containment control of linear multiagent systems with aperiodic sampling and measurement size reduction. IEEE Trans. Neural Netw. Learn. Syst. 99, 1–10 (2018)

    MathSciNet  Google Scholar 

  22. Zhang, D., Liu, L., Feng, G.: Consensus of heterogeneous linear multiagent systems subject to aperiodic sampled-data and DoS attack. IEEE Trans. Cybern. 99, 1–11 (2018)

    Google Scholar 

  23. Zhang, D., Xu, Z., Karimi, H.R., Wang, Q.-G., Yu, Li: Distributed \({ H_\infty }\) output-feedback control for consensus of heterogeneous linear multiagent systems with aperiodic sampled-data communications. IEEE Trans. Ind. Electron. 65(5), 4145–4155 (2018)

    Article  Google Scholar 

  24. Li, Z., Wang, J., Shao, H.: Delay-dependent dissipative control for linear time-delay systems. J. Frankl. Inst. 339(6–7), 529–542 (2002)

    Article  MathSciNet  MATH  Google Scholar 

  25. Gassara, H., El Hajjaji, A., Kchaou, M., Chaabane, M.: Observer based (q, v, r)-\(\alpha \)-dissipative control for T–S fuzzy descriptor systems with time delay. J. Frankl. Inst. 351(1), 187–206 (2014)

    Article  MathSciNet  MATH  Google Scholar 

  26. Sakthivel, R., Mohanapriya, S., Ahn, C.K., Selvaraj, P.: State estimation and dissipative-based control design for vehicle lateral dynamics with probabilistic faults. IEEE Trans. Ind. Electron. 65(9), 7193–7201 (2018)

    Article  Google Scholar 

  27. Sakthivel, R., Rathika, M., Santra, S., Muslim, M.: Observer-based dissipative control for Markovian jump systems via delta operators. Int. J. Syst. Sci. 48(2), 247–256 (2017)

    Article  MathSciNet  MATH  Google Scholar 

  28. Alazard, D., Apkarian, P.: Exact observer-based structures for arbitrary compensators. Int. J. Robust Nonlinear Control IFAC-Affil. J. 9(2), 101–118 (1999)

    Article  MathSciNet  MATH  Google Scholar 

  29. Wang, H., Liu, P.X., Shi, P.: Observer-based fuzzy adaptive output-feedback control of stochastic nonlinear multiple time-delay systems. IEEE Trans. Cybern. 47(9), 2568–2578 (2017)

    Article  Google Scholar 

  30. Do, T.D.: Disturbance observer-based fuzzy SMC of WECSs without wind speed measurement. IEEE Access 5, 147–155 (2017)

    Article  Google Scholar 

  31. Kim, H.S., Park, J.B., Joo, Y.H.: Sampled-data fuzzy observer design for an attitude and heading reference system and its experimental validation. J. Electr. Eng. Technol. 12(6), 2399–2410 (2017)

    Google Scholar 

  32. Fadili, Y., Lahlou, Z., Boumhidi, I.: An \(H_\infty \) T–S fuzzy observer-based controller design for wind turbine systems. In: Proceedings of the 2016 5th International Conference on Systems and Control, Morocco, pp. 163–168. IEEE (2016)

  33. Imran, R.M., Hussain, D.M., Chowdhry, B.S.: Parameterized disturbance observer based controller to reduce cyclic loads of wind turbine. Energies 11(5), 1296 (2018)

    Article  Google Scholar 

  34. Hwang, S., Park, J.B., Joo, Y.H.: Observer-based \(h_\infty \) fuzzy controller design of interval type-2 Takagi–Sugeno fuzzy systems under imperfect premise matching. Trans. Korean Inst. Electr. Eng. 66(11), 1620–1627 (2017)

    Google Scholar 

  35. Xie, X., Yue, D., Peng, C.: Multi-instant observer design of discrete-time fuzzy systems: a ranking-based switching approach. IEEE Trans. Fuzzy Syst. 25(5), 1281–1292 (2017)

    Article  Google Scholar 

  36. Xie, X., Yue, D., Peng, C.: Observer design of discrete-time fuzzy systems based on an alterable weights method. IEEE Trans. Cybern. (2018). https://doi.org/10.1109/TCYB.2018.2878419

  37. Bianchi, F.D., De Battista, H., Mantz, R.J.: Wind Turbine Control Systems. Springer, Germany (2006)

    Google Scholar 

  38. Lescher, F., Zhao, J., Borne, P.: Switching LPV controllers for a variable speed pitch regulated wind turbine. Int. J. Comput. Commun. Control 1(4), 73–84 (2006)

  39. Zhang, D., Han, Q.-L.: \(H_\infty \) control design for network-based T–S fuzzy systems with asynchronous constraints on membership functions. In: IECON 2011-37th Annual Conference on IEEE Industrial Electronics Society, pp. 2584–2589. IEEE (2011)

  40. Gahinet, P., Nemirovskii, A., Laub, A.J., Chilali, M.: The LMI control toolbox. In: Proceedings of the 33rd IEEE Conference on Decision and Control, 1994, vol. 3, pp. 2038–2041. IEEE (1994)

  41. Chang, X.-H., Yang, G.-H.: New results on output feedback \({ H_\infty }\) control for linear discrete-time systems. IEEE Trans. Autom. Control 59(5), 1355–1359 (2014)

    Article  MathSciNet  MATH  Google Scholar 

Download references

Acknowledgements

This work was partially supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2018R1A2A2A14023632) and by Korea Electric Power Corporation (Grant Number: R17XA05-17).

Author information

Authors and Affiliations

  1. School of IT Information and Control Engineering, Kunsan National University, 558, Daehak-ro, Gunsan-si, Jeollabuk-do, 54150, Korea

    Ramasamy Saravanakumar & Young Hoon Joo

Authors
  1. Ramasamy Saravanakumar
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Young Hoon Joo
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Young Hoon Joo.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

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

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Saravanakumar, R., Joo, Y.H. Fuzzy dissipative and observer control for wind generator systems: a fuzzy time-dependent LKF approach. Nonlinear Dyn 97, 2189–2199 (2019). https://doi.org/10.1007/s11071-019-05116-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11071-019-05116-0

Keywords

Search

Navigation