Nonlinear Dynamics

, Volume 70, Issue 1, pp 97–109 | Cite as

An adaptive fuzzy sliding mode controller for MEMS triaxial gyroscope with angular velocity estimation

  • Juntao Fei
  • Mingyuan Xin
Original Paper


In this paper, an adaptive fuzzy sliding mode control (AFSMC) for Micro-Electro-Mechanical Systems (MEMS) triaxial gyroscope is proposed. First, a novel adaptive identification approach with sliding mode controller which can identify angular velocity and other system parameters is developed. And in order to reduce the chattering, an AFSMC is designed to approximate the upper bound of the uncertainties and external disturbances. Based on Lyapunov methods, these adaptive laws can guarantee that the system is asymptotically stable. Numerical simulations are investigated to verify the effectiveness of the proposed AFSMC scheme.


Adaptive fuzzy sliding mode control MEMS Triaxial Gyroscope Adaptive identification Lyapunov methods 



The authors thank to the anonymous reviewers for useful comments that improved the quality of the manuscript. This work is supported by National Science Foundation of China under Grant No. 61074056, The Natural Science Foundation of Jiangsu Province under Grant No. BK2010201, and Scientific Research Foundation for the Returned Overseas Chinese Scholars, State Education Ministry.


  1. 1.
    Park, R.: Adaptive control strategies for MEMS Gyroscopes. PhD Dissertation, UC Berkeley (2000) Google Scholar
  2. 2.
    Leland, P.: Adaptive control of a MEMS gyroscope using Lyapunov methods. IEEE Trans. Control Syst. Technol. 14(2), 278–283 (2006) CrossRefGoogle Scholar
  3. 3.
    John, J., Vinay, T.: Novel concept of a single mass adaptively controlled triaxial angular rate sensor. IEEE Sens. J. 6(3), 588–595 (2006) CrossRefGoogle Scholar
  4. 4.
    Batur, C., Sreeramreddy, T.: Sliding mode control of a simulated MEMS gyroscope. ISA Trans. 45(1), 99–108 (2006) CrossRefGoogle Scholar
  5. 5.
    Sung, W., Lee, Y.: On the mode-matched control of MEMS vibratory gyroscope via phase-domain analysis and design. IEEE/ASME Trans. Mechatron. 14(4), 446–455 (2009) MathSciNetCrossRefGoogle Scholar
  6. 6.
    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
  7. 7.
    Park, S., Horowitz, R., Hong, S., Nam, Y.: Trajectory-switching algorithm for a MEMS gyroscope. IEEE Trans. Instrum. Meas. 56(60), 2561–2569 (2007) CrossRefGoogle Scholar
  8. 8.
    Sadati, N., Ghadami, R.: Adaptive multi-model sliding mode control of robotic manipulators using soft computing. Neurocomputing 71(2), 2702–2710 (2008) CrossRefGoogle Scholar
  9. 9.
    Park, B.S., Yoo, S.J., Park, J.B., Choi, Y.H.: Adaptive neural sliding mode control of nonholonomic wheeled mobile robots with model uncertainty. IEEE Trans. Control Syst. Technol. 17(1), 207–214 (2009) CrossRefGoogle Scholar
  10. 10.
    Lee, M., Choi, Y.: An adaptive neucontroller using RBFN for robot manipulators. IEEE Trans. Ind. Electron. 51(3), 711–717 (2004) CrossRefGoogle Scholar
  11. 11.
    Wang, L.: Adaptive Fuzzy Systems and Control-Design and Stability Analysis. Prentice Hall, New Jersey (1994) Google Scholar
  12. 12.
    Guo, Y., Woo, P.: An adaptive fuzzy sliding mode controller for robotic manipulators. IEEE Trans. Syst. Man Cybern., Part A, Syst. Hum. 33(2), 149–159 (2004) Google Scholar
  13. 13.
    Yoo, B., Ham, W.: Adaptive control of robot manipulator using fuzzy compensator. IEEE Trans. Fuzzy Syst. 8(2), 186–199 (2000) CrossRefGoogle Scholar
  14. 14.
    Wai, R.J.: Fuzzy sliding-mode control using adaptive tuning technique. IEEE Trans. Ind. Electron. 54(1), 586–594 (2007) CrossRefGoogle Scholar
  15. 15.
    Wai, R.J., Lin, C.M., Hsu, C.F.: Adaptive fuzzy sliding-mode control for electrical servo drive. Fuzzy Sets Syst. 143(2), 295–310 (2004) MathSciNetMATHCrossRefGoogle Scholar
  16. 16.
    Chen, B., Liu, X.S., Tong, C.: Adaptive fuzzy output tracking control of MIMO nonlinear uncertain systems. IEEE Trans. Fuzzy Syst. 15(2), 287–300 (2007) CrossRefGoogle Scholar
  17. 17.
    Ren, C., Tong, S., Li, Y.: Fuzzy adaptive high-gain-based observer backstepping control for SISO nonlinear systems with dynamical uncertainties. Nonlinear Dyn. 67(2), 941–955 (2012) MATHCrossRefGoogle Scholar
  18. 18.
    Cetin, S., Zergeroglu, E., Sivrioglu, S., Yuksek, I.: A new semiactive nonlinear adaptive controller for structures using MR damper: design and experimental validation. Nonlinear Dyn. 66(4), 731–743 (2011) MathSciNetCrossRefGoogle Scholar
  19. 19.
    Li, T., Wang, D., Chen, N.: Adaptive fuzzy control of uncertain MIMO non-linear systems in block-triangular forms. Nonlinear Dyn. 63(1–2), 105–123 (2011) MathSciNetMATHCrossRefGoogle Scholar
  20. 20.
    Wen, G., Liu, Y.: Adaptive fuzzy-neural tracking control for uncertain nonlinear discrete-time systems in the NARMAX form. Nonlinear Dyn. 66(4), 745–753 (2011) MathSciNetMATHCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2012

Authors and Affiliations

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

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