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Robust adaptive fuzzy controller with supervisory compensator for MEMS gyroscope sensor

Published online by Cambridge University Press:  10 February 2015

Yunmei Fang ,
Jian Zhou  and
Juntao Fei
Yunmei Fang
Affiliation:
College of Mechanical and Electrical Engineering, Hohai University, Changzhou, 213022, P. R. China
Jian Zhou
Affiliation:
College of IOT Engineering, Hohai University, Changzhou, 213022, P. R. China
Juntao Fei*
Affiliation:
College of IOT Engineering, Hohai University, Changzhou, 213022, P. R. China
*
*Corresponding author. E-mail: [email protected]
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Summary

In this paper, a robust adaptive fuzzy controller is proposed to improve the robustness and position tracking of a MEMS gyroscope sensor. The proposed controller is designed as an indirect adaptive fuzzy controller with a supervisory compensator. It incorporates a fuzzy inference system with an adaptive controller in a unified Lyapunov framework, which can approximate and compensate for the unknown system dynamics and nonlinearities in the MEMS gyroscope. The parameters of the membership functions in the fuzzy controller can be adjusted online based on the Lyapunov method. Moreover, a supervisory controller is employed to guarantee the asymptotic stability of the closed-loop system and boundedness of the state variables in the MEMS gyroscope model. Numerical simulations demonstrate the proposed robust adaptive fuzzy controller has satisfactory tracking performance and robustness in the presence of external disturbances.

Keywords

Adaptive fuzzy controlSupervisory compensatorApproximation error
Type
Articles
Information
Robotica , Volume 34 , Issue 10 , October 2016 , pp. 2330 - 2343
Copyright
Copyright © Cambridge University Press 2015 

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References

1. Sung, W. and Lee, Y., “On the mode-matched control of MEMS vibratory gyroscope via phase-domain analysis and design,” IEEE/ASME Trans. Mechatronics 14 (4), 446–455 (2009).CrossRefGoogle Scholar
2. Leland, R., “Adaptive control of a MEMS gyroscope using Lyapunov methods,” IEEE Trans. Control Syst. Technol. 14 (2), 278–283 (2006).CrossRefGoogle Scholar
3. Batur, C. and Sreeramreddy, T., “Sliding mode control of a simulated MEMS gyroscope,” ISA Trans. 45 (1), 99–108 (2006).CrossRefGoogle ScholarPubMed
4. Fei, J. and Batur, C., “A novel adaptive sliding mode control with application to MEMS gyroscope,” ISA Trans. 48 (1), 73–78 (2009).CrossRefGoogle ScholarPubMed
5. Antonello, R., Oboe, L., Prandi, F. and Biganzoli, F., “Automatic mode matching in MEMS vibrating gyroscopes using extremum-seeking control,” IEEE Trans. Ind. Electron. 56 (10), 3880–3891 (2009).CrossRefGoogle Scholar
6. Park, R., Horowitz, R., Hong, S. and Nam, Y., “Trajectory-switching algorithm for a MEMS gyroscope,” IEEE Trans. Instrum. Meas. 56 (60), 2561–2569 (2007).CrossRefGoogle Scholar
7. Wang, L., Adaptive Fuzzy Systems and Control-Design and Stability Analysis (Prentice Ha1l, New Jersey, USA, 1994).Google Scholar
8. Guo, Y. and Woo, P., “An adaptive fuzzy sliding mode controller for robotic manipulators,” IEEE Trans. Syst. Man Cybern. A 33 (2), 149–159 (2004).Google Scholar
9. Tong, S. and Zhou, J., “Design and stability of fuzzy indirect and direct adaptive control for nonlinear system,” J. Control Decis. 15 (3), 294–296 (2000).Google Scholar
10. Yoo, B. and Ham, W., “Adaptive control of robot manipulator using fuzzy compensator,” IEEE Trans. Fuzzy Syst. 8 (2), 186–199 (2000).Google Scholar
11. Wai, R. and Yang, Z., “Adaptive fuzzy neural network control design via a T–S fuzzy model for a robot manipulator including actuator dynamics,” IEEE Trans. Syst. Man Cybern. B 38 (5), 1326–1346 (2008).Google Scholar
12. Lee, H., “Robust adaptive fuzzy control by backstepping for a class of MIMO nonlinear systems,” IEEE Trans. Fuzzy Syst. 19 (2), 265–275 (2011).CrossRefGoogle Scholar
13. Islam, S. and Liu, P. X., “Robust adaptive fuzzy output feedback control system for robot manipulators,” IEEE/ASME Trans. Mechatronics 16 (2), 288–296 (2011).CrossRefGoogle Scholar
14. Chang, Y., Chang, C., Chen, C. and Tao, C., “Fuzzy sliding-mode formation control for multirobot systems: Design and implementation,” IEEE Trans. Syst. Man Cybern. B 42 (2), 444–457 (2012).CrossRefGoogle ScholarPubMed
15. Tao, C., Taur, J., Chang, J. and Su, S., “Adaptive fuzzy switched swing-up and sliding control for the double-pendulum-and-cart system,” IEEE Trans. Syst. Man Cybern. B 40 (1), 241–252 (2010).Google ScholarPubMed
16. Rastovic, D., “Targeting and synchronization at tokamak with recurrent artificial neural networks,” Neural Comput. Appl. 21 (5), 1065–1069 (2012).CrossRefGoogle Scholar
17. Fei, J. and Zhou, J., “Robust adaptive control of MEMS triaxial gyroscope using fuzzy compensator,” IEEE Trans. Syst. Man Cybern. B 42 (6), 1599–1607 (2012).Google ScholarPubMed
18. Fei, J. and Yang, Y., “Comparative study of system identification approaches for adaptive tracking of MEMS gyroscope,” Int. J. Robot. Autom. 27 (30), 322–329 (2012).Google Scholar
19. Ioannou, P. and Sun, J., Robust Adaptive Control (Prentice-Hall, Upper Saddle River, New Jersey, USA, 1996).Google Scholar