Input Force Estimation Using System Identification Techniques: Kalman Filter with Recursive Least Square Method versus Time Domain Convolution Method
Date Issued
2009
Date
2009
Author(s)
Chen, Chia-Hui
Abstract
In this study several input force identification methods is presented using the direct response meausrements. First, by assuming the input force as a summation of sine and cosine functions, the time domain convolution method is derived. Based on the system convolution vector and the optimization scheme are presented for identifying the input forces. Then a different input force identification approach which employs the Recursive Least Square with Kalman Filter (RLS-KF) method is used for comparison. Different to the above two proposed methods, a sensitivity method with respect to the input force parameters is also presented for identify the force parameters by estabilishing sensitivity matrix of dynamic response. Optimization with respect to the force parameters can improve the efficiency of sensitivity method is also investigated. PSD method for identifying cross power spectral density of distributed excitation force is discussed by calculating cross power spectral density of measured response. Due to the limited number of measurements on the response data, both Kalman Estimator technique and Singular Value Decomposition (SVD) incopoerated with piecewise cubic Hermite Polynomial curve fitting method are used to reconstruct the unmeasured reponses and establish the full response measurement. The proposed input force identification methods were verified first using numerical simulation of a MDOF system subjected to limited number of input forces. Second, data from the shaking table tests of soil-pile interaction are used to idrntify the interaction forces. For this large-scale shaking table test, the ambient vibration measurements of the pile are used to estabilish the structural system matrix. Based on the proposed identification methods in this study, soil-pile force is identified.
Subjects
Input identification
system identification
response reconstruction
Kalman filter
recursive least-squares estimator
soil-pile interaction
Type
thesis
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