Hostname: page-component-586b7cd67f-rcrh6 Total loading time: 0 Render date: 2024-11-29T19:05:47.264Z Has data issue: false hasContentIssue false

Inverse Active Load Inputs Estimation of the 3D Spatial Truss Structure System

Published online by Cambridge University Press:  07 December 2011

M.-H. Lee*
Affiliation:
Department of Civil Engineering, Chinese Military Academy, Fengshan, Kaohsiung, Taiwan 83059, R.O.C.
*
*Assistant Professor, corresponding author
Get access

Abstract

This study presents an innovative fuzzy inverse method with the finite-element scheme for estimating the unknown time-varying load inputs on a three-dimensional (3D) spatial truss structural system. The finite-element scheme is employed to discretize the problem in space, allowing multidimensional problems of various geometries to be treated. This method is based on the fuzzy Kalman Filter (FKF) technology and the fuzzy weighting recursive least square method (FWRLSM). The fuzzy Kalman filter measures the system responses at two distinct nodes in the 3D spatial truss structure. The fuzzy weighting recursive least square method is derived using the residual innovation sequence to compute the input loads. The proposed method's superiority is demonstrated using several typical simulation cases that vary with different estimator and the distinct levels of the initial process noise covariance and the measurement noise covariance. The results show that this method has great stability and accuracy.

Type
Articles
Copyright
Copyright © The Society of Theoretical and Applied Mechanics, R.O.C. 2011

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

1.Yang, Y. B. and Yau, J. D., “Vehicle-Bridge Interaction Element for Dynamic Analysis,” Journal of Structural Engineering, ASCE, 123, pp. 15121518 (1997).CrossRefGoogle Scholar
2.Groetsch, C. W., “Inverse Problem in the Mathematical Sciences,” Braunschweig: Vieweg, 152 (1993).Google Scholar
3.Stevens, K. K., “Force Identification Problem-An Overview,” In SEM Spring Conference on Experimental Mechanics, Houston, Texas: SEM, (1987).Google Scholar
4.Tuan, P. C., Fong, L. W. and Huang, W. T., “Analysis of On-Line Inverse Heat Conduction Problems,” Journal of Chung Cheng Institute of Technology, 25, pp. 5973 (1996)Google Scholar
5.Tuan, P. C., Lee, S. C. and Hou, W. T., “An Efficient On-Line Thermal Input Estimation Method Using Kalman Filter and Recursive Least Square Algorithm,” Inverse Problem Engineering, 5, pp. 309333 (1997).CrossRefGoogle Scholar
6.Ma, C. K., Chang, J. M. and Lin, D. C., “Input Forces Estimation of Beam Structures by an Inverse Method,” Journal of Sound and Vibration, 259, pp. 387407 (2003).CrossRefGoogle Scholar
7.Lee, M. H. and Chen, T. C., “Blast Load Input Estimation of the Medium Girder Bridge Using Inverse Method,” Defence Science Journal, 58, pp. 4656 (2008).CrossRefGoogle Scholar
8.Chen, T. C. and Lee, M. H., “Inverse Active Wind Load Inputs Estimation of the Multilayer Shearing Stress Structure,” Wind and Structures, An International Journal, 11, pp. 1933 (2008).CrossRefGoogle Scholar
9.Chan, Y. T., Hu, A. G. C. and Plant, J. B., “A Kalman Filter Based Tracking Scheme with Input Estimation,” IEEE Transactions on Aerospace and Electronic Systems, AES-15, pp. 237244 (1979).CrossRefGoogle Scholar
10.Bogler, P. L., “Tracking a Maneuvering Target Using Input Estimation,” IEEE Transactions on Aerospace and Electronic Systems, AES-23, pp. 298310 (1987).CrossRefGoogle Scholar
11.Wang, L. X., Adaptive Fuzzy Systems and Control: Design and Stability Analysis, Prentice-Hall, Englewood Cliffs, NJ (1994).Google Scholar
12.Kwon, Y. W. and Bang, H. C., The finite element method using MATLAB. CRC Press, Boca Raton (2000).Google Scholar
13. Defining and Testing Dynamic Parameters in High-Speed ADCs, Part 1-Maxim Integrated Products Application note 728 (2001).Google Scholar