Volume 20, Issue 7 (July 2020)                   Modares Mechanical Engineering 2020, 20(7): 1851-1859 | Back to browse issues page

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Mirzaei M, Hosseini I. Compensation of Thermal Bias for Micro-Electro-Mechanical Rate Gyroscope by Using Extended Kalman Filter in Accelerated Motions. Modares Mechanical Engineering 2020; 20 (7) :1851-1859
URL: http://mme.modares.ac.ir/article-15-34983-en.html
1- “Hydro-Aeronautical Research Center” and “School of Mechanical Engineering”, Shiraz University, Shiraz, Iran , mmirzaei@shirazu.ac.ir
2- School of Electrical Engineering, Shiraz University, Shiraz, Iran
Abstract:   (2376 Views)
Initial bias is a random parameter in micro-electro-mechanical rate gyroscopes that changes with each turn on and turn off. The bias can be estimated by averaging in static condition or by extended Kalman filter in other conditions. In addition, this parameter is affected by temperature or linear acceleration. Curve fitting on the bias variation of micro-electro-mechanical rate gyroscopes due to thermal effects is a usual method for thermal compensation of these sensors. However, these approximate curves cannot completely compensate the effect of the thermal bias in long-time applications. In this study, it is tried to improve the calculating accuracy by a combination of extended Kalman filter and the results of these curves and using advantages of both methods. Also bias estimation is improved using the switching algorithm in accelerated motions by avoiding improper data in the estimation process. Experimental tests show the effectiveness of this method especially in long-time accelerated motions.
Full-Text [PDF 685 kb]   (1244 Downloads)    
Article Type: Original Research | Subject: Mechatronics
Received: 2019/07/20 | Accepted: 2020/04/8 | Published: 2020/07/20

References
1. Walther A, Le Blanc C, Delorme N, Deimerly Y, Anciant R, Willemin J. Bias contributions in a MEMS tuning fork gyroscope. Journal of Microelectromechanical Systems. 2013;22(2):303-308. [Link] [DOI:10.1109/JMEMS.2012.2221158]
2. Feng R, Bahari J, Jones JD, Leung AM. MEMS thermal gyroscope with self-compensation of the linear acceleration effect. Sensors and Actuators A: Physical. 2013;203:413-420. [Link] [DOI:10.1016/j.sna.2013.09.017]
3. Narasimhappa M, Sabat SL, Peesapati R, Nayak J. An innovation based random weighting estimation mechanism for denoising fiber optic gyro drift signal. Optik. 2014;125(3):1192-1198. [Link] [DOI:10.1016/j.ijleo.2013.07.161]
4. Mirzaei M, Hosseini I. Robust ellipsoid fitting method based on optimization of a novel nonlinear cost function in navigation systems. Journal of the Brazilian Society of Mechanical Sciences Engineering. 2019;41(6):247-256. [Link] [DOI:10.1007/s40430-019-1747-2]
5. Cui J, Zhao Q, Yan GJM. Effective bias warm-up time reduction for MEMS gyroscopes based on active suppression of the coupling stiffness. Microsystems and Nanoengineering. 2019;5(1):1-12. [Link] [DOI:10.1038/s41378-019-0057-2]
6. Cui M, Huang Y, Wang W, Cao H. MEMS gyroscope temperature compensation based on drive mode vibration characteristic control. Micromachines. 2019;10(4):248. [Link] [DOI:10.3390/mi10040248]
7. Xia D, Chen S, Wang S, Li H. Microgyroscope temperature effects and compensation-control methods. Sensors. 2009;9(10):8349-8376. [Link] [DOI:10.3390/s91008349]
8. Zhang C, Wu Q-S, Yin T, Yang H-G. A MEMS gyroscope readout circuit with temperature compensation. In Nano/Micro Engineered and Molecular Systems (NEMS), 5th IEEE International Conference, 20-23 Junaury 2010, Xiamen, China. Xiamen: IEEE; 2010. [Link]
9. Sun H, Jia k, Liu X, Yan G, Hsu Y-W, Fox RM, et al. A CMOS-MEMS gyroscope interface circuit design with high gain and low temperature dependence. IEEE Sensors Journal. 2011;11(11):2740-2748. [Link] [DOI:10.1109/JSEN.2011.2158819]
10. Yin T, Wu H, Wu Q, Yang H, Jiao J. A TIA-based readout circuit with temperature compensation for MEMS capacitive gyroscope. In Nano/Micro Engineered and Molecular Systems (NEMS), IEEE International Conference, 20-23 February 2011, Kaohsiung, Taiwan. Kaohsiung: IEEE: 2011. [Link] [DOI:10.1109/NEMS.2011.6017377]
11. Prikhodko IP, Trusov AA, Shkel AM. Compensation of drifts in high-Q MEMS gyroscopes using temperature self-sensing. Sensors and Actuators A: Physical. 2013;201:517-524. [Link] [DOI:10.1016/j.sna.2012.12.024]
12. Chiu S-R, Sue Ch-Y, Lin Ch-H, Teng L-T, Liao L-P, Hsu Y-W, et al. Active thermal compensation of MEMS based gyroscope. In Sensors, 2012 IEEE, 28-31 October 2012, Taipei, Taiwan. Taipei: IEEE; 2013. [Link] [DOI:10.1109/ICSENS.2012.6411164]
13. Zhang Q, Tan Z, Guo L. Compensation of temperature drift of MEMS gyroscope using BP neural network. In International Conference on Information Engineering and Computer Science, 19-20 December 2009, Wuhan, China. Wuhan: IEEE; 2009. [Link] [DOI:10.1109/ICIECS.2009.5365140]
14. Bekkeng JK. Calibration of a novel MEMS inertial reference unit. IEEE Transactions on instrumentation and measurement. 2009;58(6):1967-1974. [Link] [DOI:10.1109/TIM.2008.2006126]
15. Liu Y, Liu C, Xu J, Zhao X. Research on temperature compensation technology of micro-electro-mechanical systems gyroscope in strap-down inertial measurement unit. In The Euro-China Conference on Intelligent Data Analysis and Applications, 25 December 2018. Berlin: Springer; 2018. [Link]
16. Nez A, Fradet L, Laguillaumie P, Monnet T, Lacouture P. Simple and efficient thermal calibration for MEMS gyroscopes. Medical engineering physics. 2018;55:60-67. [Link] [DOI:10.1016/j.medengphy.2018.03.002]
17. Zhang Q, Wang L, Gao P, Liu Z. An innovative wavelet threshold denoising method for environmental drift of fiber optic gyro. Mathematical Problems in Engineering. 2016;2016:1-8. [Link] [DOI:10.1155/2016/9017481]
18. Huaming Q, Jichen M. Research on fiber optic gyro signal de-noising based on wavelet packet soft-threshold. Journal of Systems Engineering and Electronics. 2009;20(3):607-612 [Link]
19. Dang Sh-W, Li L-J, Wang Q-Q, Wang K-L, Cheng P-Zh. Fiber optic gyro noise reduction based on hybrid CEEMDAN-LWT method. Measurement. 2020;161:107865-107877. [Link] [DOI:10.1016/j.measurement.2020.107865]
20. 20 Shiau J-K, Huang C-X, Chang M-Y. Noise characteristics of MEMS gyro's null drift and temperature compensation. Applied Science and Engineering. 2012;15(3):239-246. [Link]
21. Fontanella R, Accardo D, Moriello RSL, Angrisani L, De Simone D. MEMS gyros temperature calibration through artificial neural networks. Sensors and Actuators A: Physical. 2018;279:553-565. [Link] [DOI:10.1016/j.sna.2018.04.008]
22. Fontanella R, Accardo D, Moriello R, Angrisani L, Simone D. An innovative strategy for accurate thermal compensation of Gyro Bias in inertial units by exploiting a novel Augmented Kalman Filter. Sensors. 2018;18(5):1457. [Link] [DOI:10.3390/s18051457]
23. Hu G, Zhang Zh, Armaou A, Yan Zh. Robust extended Kalman filter based state estimation for nonlinear dynamic processes with measurements corrupted by gross errors. Journal of the Taiwan Institute of Chemical Engineers. 2020;106:20-33. [Link] [DOI:10.1016/j.jtice.2019.10.015]
24. Titterton D, Weston JL, Weston J. Strapdown inertial navigation technology. Stevenage: IET; 2004. [Link] [DOI:10.1049/PBRA017E]

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