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JACIII Vol.26 No.6 pp. 965-973
doi: 10.20965/jaciii.2022.p0965
(2022)

Paper:

Two Degree of Freedom Dynamic Model Parameter Identification of Accelerometer Using Feature Point Coordinate Estimation and Amplitude Correction

Qingxuan Wei and Xueting Li

Information Engineering College, Beijing Institute of Petrochemical Technology
No.19 Qingyuanbei Road, Daxing District, Beijing 102617, China

Corresponding author

Received:
March 14, 2022
Accepted:
July 6, 2022
Published:
November 20, 2022
Keywords:
accelerometer, two degree of freedom dynamic model, feature point coordinate estimation, amplitude correction
Abstract

The accurate identification and characterization of the accelerometer dynamic model parameters play an important role in improving the dynamic performance of the device or system with an accelerometer. To overcome the problem that the traditional single degree of freedom (SDF) dynamic model of the accelerometer cannot describe the dynamic characteristics beyond the first resonant frequency of the accelerometer, a two degree of freedom (TDF) dynamic model of the accelerometer was constructed. On this basis, a parameter identification method for the TDF dynamic model of the accelerometer based on the feature points coordinate estimation and amplitude correction was proposed. First, the zero frequency point coordinates of the accelerometer frequency response were obtained by the Hv method. The first and second resonance point coordinates were estimated by discrete spectrum correction and the least square (DSC-LS) method. Then, the amplitude correction coefficient was applied to eliminate the influence of series coupling on the amplitude. Finally, the TDF dynamic model parameters of the accelerometer were calculated through the feature point coordinates. The experimental results show that the method has high accuracy and can avoid the influence of series coupling on the parameter identification accuracy of the accelerometer’s TDF dynamic model without complex derivation and decoupling operations. The identified TDF dynamic model of the accelerometer can represent the dynamic characteristics with a higher frequency range.

Cite this article as:
Q. Wei and X. Li, “Two Degree of Freedom Dynamic Model Parameter Identification of Accelerometer Using Feature Point Coordinate Estimation and Amplitude Correction,” J. Adv. Comput. Intell. Intell. Inform., Vol.26 No.6, pp. 965-973, 2022.
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References
  1. [1] I. A. Sulistijono, S. Kuswadi, and O. Setiaji, “A study on fuzzy control of humanoid soccer robot EFuRIO for vision control system and walking movement,” J. Adv. Comput. Intell. Intell. Inform., Vol.16, No.3, pp. 444-452, 2012.
  2. [2] M. Niitsuma, T. Ochi, and M. Yamaguchi, “Design of mutual interaction between a user and smart electric wheelchair,” J. Adv. Comput. Intell. Intell. Inform., Vol.16, No.2, pp. 305-312, 2012.
  3. [3] N. M. H. Basri, K. S. M. Sahari, and A. Anuar, “Development of a robotic boiler header inspection device with redundant localization system,” J. Adv. Comput. Intell. Intell. Inform., Vol.18, No.3, pp. 451-458, 2014.
  4. [4] M. Gadebe, O. P. Kogeda, and S. Ojo, “Smartphone naïve bayes human activity recognition using personalized datasets,” J. Adv. Comput. Intell. Intell. Inform., Vol.24, No.5, pp. 685-702, 2020.
  5. [5] A. Link and H. J. V. Martens, “Accelerometer identification using shock excitation,” Measurement, Vol.35, No.2, pp. 191-199, 2004.
  6. [6] A. Link, A. Täubner, and W. Wabinski, “Modelling accelerometers for transient signals using calibration measurements upon sinusoidal excitation,” Measurement, Vol.40, Nos.9-10, pp. 928-935, 2006.
  7. [7] A. Link, A. Tauübner, and W. Wabinski, “Calibration of accelerometers: determination of amplitude and phase response upon shock excitation,” Meas. Sci. Technol., Vol.17, No.7, pp. 1888-1894, 2006.
  8. [8] C. Elster, A. Link, and T. Bruns, “Analysis of dynamic measurements and determination of time-dependent measurement uncertainty using a second-order model,” Meas. Sci. Technol., Vol.18, No.12, pp. 3682-3687, 2007.
  9. [9] T. Bruns, A. Link, and F. Schmähling, “Calibration of accelerometers using parameter identification – targeting a versatile new standard,” 19th XIX IMEKO World Congress Fundamental and Applied Metrology, pp. 2485-2489, 2009.
  10. [10] Y. Wang, J. Fan, and J. Zu, “Quasi-static calibration method of a high-g accelerometer,” Sensors, Vol.17, No.2, pp. 409-421, 2017.
  11. [11] R. Ling, C. Yi, and N. Li, “Hybrid genetic algorithm based on mutative scale chaos optimization dynamic modeling method for accelerometer,” The 7th World Congress on Intelligent Control and Automation, pp. 6512-6515, 2008.
  12. [12] Z. X. Yang, H. J. Du, and J. Fan, “Dynamic Modeling of Acceleration Sensor Based on Generalized Least Square Method,” Chin. J. Sens. Actuators, 2009.
  13. [13] Z. K. Yang, J. L. Wang, and T. Yu, “Dynamic model parameter identification of the acceleration sensor based on the prediction error method,” Chin. J. Sci. Instrum., Vol.36, No.6, pp. 1244-1249, 2015.
  14. [14] T. Yu, B. Y. Hao, and J. L. Wang, “An accelerometer modeling approach based on mixed-kernel support vector machine,” 2016 12th World Congress on Intelligent Control and Automation (WCICA), pp. 2760-2764, 2016.
  15. [15] H. B. Hu and Q. Sun, “Parameter identification of accelerometer based on primary shock calibration method,” J. of Test and Measurement Technology, Vol.27, No.01, pp. 19-24, 2013.
  16. [16] Q. X. Wei, J. L. Wang, and X. S. Fu, “Dynamic model parameter identification of accelerometer using delay time correction and least square,” Chin. J. Sci. Instrum., Vol.38, No.12, pp. 71-79, 2017.
  17. [17] Q. X. Wei, J. L. Wang, and R. Han, “Dynamic model parameter identification of accelerometer using discrete spectrum correction and least square,” Trans. Beijing Inst. Technol., Vol.38, No.12, pp. 1282-1288, 2018.
  18. [18] J. L. Wang, Y. Q. Guo, and Q. X. Wei, “Accelerometer dynamic model parametric identification using WLS-SVM,” J. of Vibration and Shock, Vol.37, No.19, pp. 239-244+253, 2018.
  19. [19] G. H. Jia, J. J. Zhang, and H. M. Zheng, “Research on FFT+ FT Spectrum Zooming Method for Differential Optical Absorption Spectroscopy,” Spectrosc. Spect. Anal, Vol.41, No.7, pp. 2116-2121, 2021.
  20. [20] H. B. Lin, K. Ding, and C. Y. Xu, “Anti-noise performance of the FT continuous zoom analysis method for discrete spectrum,” Chin. J. Mech. Eng., Vol.23, No.6, pp. 774-779, 2010.
  21. [21] ISO 16063-13:2001, “Methods for the calibration of vibration and shock transducers-part 13: Primary shock calibration using laser interferometry,” France: Bureau International des Poids et Measures, 2001.

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