Hostname: page-component-7b7567c8c-mnklg Total loading time: 0 Render date: 2026-11-12T20:11:52.827Z Has data issue: false hasContentIssue false
  • English
  • Français

Robust neural network control of MEMS gyroscope using adaptive sliding mode compensator

Published online by Cambridge University Press:  04 July 2014

Juntao Fei and
Yuzheng Yang
Juntao Fei*
Affiliation:
Jiangsu Key Laboratory of Power Transmission and Distribution Equipment Technology, Hohai University, Changzhou, China
Yuzheng Yang
Affiliation:
College of IOT Engineering, Hohai University, Changzhou, China
*
*Corresponding author. E-mail: jtfei@yahoo.com
Rights & Permissions [Opens in a new window]

Summary

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the 'Save PDF' action button.

A new robust neural sliding mode (RNSM) tracking control scheme using radial basis function (RBF) neural network (NN) is presented for MEMS z-axis gyroscope to achieve robustness and asymptotic tracking error convergence. An adaptive RBF NN controller is developed to approximate and compensate the large uncertain system dynamics, and a robust compensator is designed to eliminate the impact of NN modeling error and external disturbances for guaranteeing the asymptotic stability property. Moreover, another RBF NN is employed to learn the upper bound of NN modeling error and external disturbances, so the prior knowledge of the upper bound of system uncertainties is not required. All the adaptive laws in the RNSM control system are derived in the same Lyapunov framework, which can guarantee the stability of the closed loop system. Comparative numerical simulations for an MEMS gyroscope are investigated to verify the effectiveness of the proposed RNSM tracking control scheme.

Keywords

MEMS gyroscopeSliding mode controlBound estimationNeural network

Information

Type
Articles
Information
Robotica , Volume 34 , Issue 3 , March 2016, pp. 497 - 512
Copyright
Copyright © Cambridge University Press 2014 

References

1.Park, S., “Adaptive control of a vibratory angle measuring gyroscope,” Sensors 10 (9), 8478–8490 (2010).CrossRefGoogle ScholarPubMed
2.Leland, R. P., “Adaptive control of a MEMS gyroscope using Lyapunov methods,” IEEE Trans. Control Syst. Technol. 14 (2), 278–283 (2006).CrossRefGoogle Scholar
3.Salah, M., McIntyre, M. and Dawson, D., “Sensing of the time-varying angular rate for MEMS Z-axis gyroscopes,” Mechatronics 20 (6), 720–727 (2010).CrossRefGoogle Scholar
4.Wang, C. and Yau, H., “Nonlinear dynamic analysis and sliding mode control for a gyroscope system,” Nonlinear Dyn. 66 (1), 53–65 (2011).CrossRefGoogle Scholar
5.Fei, J. and Yang, Y., “Comparative study of system identification approaches for adaptive tracking of MEMS gyroscope,” Int. J. Robot. Autom. 27 (3), 322–329 (2012).Google Scholar
6.Ebrahimi, B. and Vali, M., “Terminal sliding mode control of Z-axis MEMS gyroscope with observer based rotation rate estimation,” Proceedings of the American Control Conference (2011), pp. 3483–3489.Google Scholar
7.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
8.Wai, R. J., “Tracking control based on neural network strategy for robot manipulator,” Neurocomputing 51, 425–445 (2003).CrossRefGoogle Scholar
9.Patompark, P. and Nilkhamhang, I., “Adaptive backstepping sliding-mode controller with bound estimation for underwater robotics vehicles,” Proceedings of the 9th International Conf. on Electronics, Computer, Telecommunications and Information Technology (2012) pp. 1–4.Google Scholar
10.Lewis, F., Yesildirek, A. and Liu, K., “Multilayer neural-net robot controller with guaranteed performance,” IEEE Trans. Neural Netw. 7 (2), 388–399 (1996).CrossRefGoogle Scholar
11.Feng, G., “A compensating scheme for robot tracking based on neural networks,” Robot. Auton. Syst. 15 (3), 199–206 (1995).CrossRefGoogle Scholar
12.Lin, F., Chen, S. and Shyu, K., “Robust dynamic sliding-mode control using adaptive RENN for magnetic levitation system,” IEEE Trans. Neural Netw. 20 (6), 938–951 (2009).Google ScholarPubMed
13.Man, Z., Wu, H., Liu, S. and Yu, X., “A new adaptive backpropagation algorithm based on Lyapunov stability theory for neural networks,” IEEE Trans. Neural Netw. 17 (6), 1580–1591 (2006).Google ScholarPubMed
14.Moody, J. and Darken, C. J., “Fast learning in networks of locally-tuned processing units,” Neural Comput. 1 (2), 281–298 (1989).CrossRefGoogle Scholar
15.Holcomb, T. and Morari, M., “Local training for radial basis function networks: Towards solving the hidden unit problem,” Proceedings of the American Control Conference (1991) pp. 2331–2336.Google Scholar
16.Park, J. and Sandberg, I., “Universal approximation using radial-basis-function networks,” Neural Comput. 3 (2), 246–257 (1991).CrossRefGoogle ScholarPubMed
17.Albertini, F. and Sontag, E. D., “For neural nets, function determines form,” Proceedings of the 31st IEEE Conference on Decision and Control (1992) pp. 26–31.Google Scholar
18.Grimm, W., “Robot non-linearity bounds evaluation techniques for robust control,” Int. J. Adapt. Control Signal Process. 4 (6), 501–522 (1990).CrossRefGoogle Scholar
19.Astrom, K. and Wittenmark, B., Adaptive Control (Addison-Wesley, New York, 1995).Google Scholar
20.Slotine, J. and Li, W., Applied Nonlinear Control (Prentice-Hall, Englewood Cliffs, NJ, 1991).Google Scholar
21.Fei, J., Ding, H., “Adaptive sliding mode control of dynamic system using RBF neural network,” Nonlinear Dyn. 70 (2), 1563–1573 (2012).CrossRefGoogle Scholar
22.Fei, J. and Xin, M., “An adaptive fuzzy sliding mode controller for MEMS triaxial gyroscope with angular velocity estimation,” Nonlinear Dyn 70 (1), 97–109 (2012).CrossRefGoogle Scholar
23.Park, S. and Horowitz, R., “Adaptive control for the conventional mode of operation of MEMS gyroscope,” J. Microelectromech. Syst. 12 (1), 101–108 (2003).CrossRefGoogle Scholar
24.Fei, J. and Ding, H., “System dynamics and adaptive control for MEMS gyroscope sensor,” Int. J. Adv. Robot. Syst. 7 (4), 81–86 (2010).CrossRefGoogle Scholar