Nonlinear Dynamics

, Volume 77, Issue 1–2, pp 361–371 | Cite as

Robust adaptive sliding mode control of MEMS gyroscope using T–S fuzzy model

  • Shitao Wang
  • Juntao Fei
Original Paper


In this paper, a multi-input multi-output Takagi–Sugeno (T–S) fuzzy model is proposed to represent the nonlinear model of micro-electro mechanical systems (MEMS) gyroscope and improve the tracking and compensation performance. A robust adaptive sliding mode control with on-line identification for the upper bounds of external disturbances and an adaptive estimator for the model uncertainty parameters are proposed in the Lyapunov framework. The adaptive algorithm of model uncertainty parameters could compensate the error between the optimal T–S model and the designed T–S model, and decrease the chattering of the sliding surface. Based on Lyapunov methods, these adaptive laws can guarantee that the system is asymptotically stable. For the purpose of comparison, the designed controller is also implemented on the nonlinear model of MEMS gyroscope. Numerical simulations are investigated to verify the effectiveness of the proposed control scheme on the T–S model and the nonlinear model.


Adaptive sliding mode control Sliding surface T–S fuzzy model Upper bound of external disturbance 



The authors thank to the anonymous reviewers for useful comments that improved the quality of the manuscript .This work is partially supported by National Science Foundation of China under Grant No. 61374100; Natural Science Foundation of Jiangsu Province under Grant No. BK20131136. The Fundamental Research Funds for the Central Universities under Grant No. 2013B19314.


  1. 1.
    Park, R., Horowitz, R., Hong, S., Nam, Y.: Trajectory switching algorithm for a MEMS gyroscope. IEEE Trans. Instrum. Meas. 56(60), 2561–2569 (2007) Google Scholar
  2. 2.
    Leland, R.: Adaptive control of a MEMS gyroscope using Lyapunov methods. IEEE Trans. Control Syst. Technol. 14, 278–283 (2006)CrossRefGoogle Scholar
  3. 3.
    John, J., Vinay, T.: Novel concept of a single mass adaptively controlled triaxial angular velocity sensor. IEEE Sens. J. 6(3), 588–595 (2006)CrossRefGoogle Scholar
  4. 4.
    Utkin, V.I.: Variable structure systems with sliding modes. Trans. Autom. Control 22, 212–222 (1977)CrossRefzbMATHMathSciNetGoogle Scholar
  5. 5.
    Batur, C., Sreeramreddy, T.: Sliding mode control of asimulated MEMS gyroscope. ISA Trans. 45(1), 99–108 (2006)CrossRefGoogle Scholar
  6. 6.
    Fei, J., Batur, C.: A novel adaptive sliding mode control with application to MEMS gyroscope. ISA Trans. 48(1), 73–78 (2009)CrossRefGoogle Scholar
  7. 7.
    Fei, J., Chowdhury, F.: Robust adaptive sliding mode controller for triaxial gyroscope, the 28th Chinese Control Conference Shanghai, P.R. China 5574–5579, (2009)Google Scholar
  8. 8.
    Fei, J.: Robust adaptive vibration tracking control for a MEMS vibratory gyroscope with bound estimation. IET Control Theory Appl. 4(6), 1019–1026 (2010)CrossRefGoogle Scholar
  9. 9.
    Fei, J., Xin, M.: An adaptive fuzzy sliding mode controller for MEMS triaxial gyroscope with angular velocity estimation. Nonlinear Dyn. 70, 97–109 (2012)Google Scholar
  10. 10.
    Fei, J., Wang, S.: Feedback linearization-based adaptive fuzzy sliding mode control of MEMS triaxial gyroscope. Int. J. Robot. Autom. 28(1), 72–80 (2013)Google Scholar
  11. 11.
    Fei, J., Zhou, J.: Robust adaptive control of MEMS triaxial gyroscope using fuzzy compensator. IEEE Trans. Syst. Man Cybern. Part B 42(6), 1599–1607 (2012)CrossRefGoogle Scholar
  12. 12.
    Asokanthan, S., Wang, T.: Nonlinear instabilities in a vibratory gyroscope subjected to angular speed fluctuations. Nonlinear Dyn. 54, 69–78 (2008)CrossRefzbMATHGoogle Scholar
  13. 13.
    Wang, T.: Nonlinear and stochastic dynamics of MEMS-based angular rate sensing and switching systems, Ph.D Dissertation, University of Western of Ontario London, Ontario, Canada (2009)Google Scholar
  14. 14.
    Takagi, T., Sugeno, M.: Fuzzy identification of systems and its applications to modeling and control. IEEE Trans. Syst. Man Cybern. Part B 15, 116–132 (1985)CrossRefzbMATHGoogle Scholar
  15. 15.
    Teixeira, M., Stanislaw, H.: Stabilizing controller design for uncertain nonlinear systems using fuzzy models. IEEE Trans. Fuzzy Syst. 7(2), 133–142 (1999)CrossRefGoogle Scholar
  16. 16.
    Chien, Y., Wang, W., Leu, Y., Lee, T.: Robust adaptive controller design for a class of uncertain nonlinear systems using online T–S fuzzy-neural modeling approach. IEEE Trans. Syst. Man Cybern. Part B 41(2), 542–552 (2011)CrossRefGoogle Scholar
  17. 17.
    Park, C., Cho, Y.: T–S model based indirect adaptive fuzzy control using online parameter estimation. IEEE Trans. Syst. Man Cybern. Part B 34(6), 2293–2302 (2004)CrossRefGoogle Scholar
  18. 18.
    Su, C.Y., Leung, T.: A sliding mode controller with bound-estimation for robot manipulator. IEEE Trans. Robot. Autom. 9(2), 208–214 (1993)CrossRefGoogle Scholar
  19. 19.
    Feng, G.: A survey on analysis and design of model-based fuzzy control systems. IEEE Trans. Fuzzy Syst. 14(2), 676–697 (2003)Google Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2014

Authors and Affiliations

  1. 1.Jiangsu Key Laboratory of Power Transmission and Distribution Equipment Technology, College of Computer and InformationHohai UniversityChangzhouChina

Personalised recommendations