On-Line Input Force Identification Using Kalman Filter Technique
Date Issued
2010
Date
2010
Author(s)
Lai, Shih-Ming
Abstract
Abstract (In English)
In this study several input force identification methods is presented using the direct response measurements. Consider the situation of without the unknown input term in the measurement equation, four input force identification methods, RLS-KF Method, Modified KF Input ID Using State Estimation, Input Estimation through Direct Operation & Covariance Filtering, and Observer Technique for Unknown Input Estimation, are used to deal with the above problem. The method of KF for Input ID Using Acceleration Responses is used when the measurement equation include both the state response and excitation term.
The proposed input force identification methods were verified first using numerical simulation of a simple supported beam. After verified all of the above input force identification methods are practicable, the responses from two different experiments are used to identify the external forces.
First, a simple beam structure with fixed end is designed and installs strain gauges on the bottom of the beam evenly. Because of the strain gauge can be easily installed onto structures and the price is not expensive, it is suggested to be the measurement sensor in the designed beam structure and provide a method to transfer the strain data to displacement data. Consequently, the external force can be identified from the strain response directly through the input estimation algorithm which are proposed.
Second, data from the shaking table tests of soil-pile interaction are used to identify the interaction forces. For this large-scale shaking table test, the ambient vibration measurement of the pile is used to establish the structural system matrix. Based on the proposed identification methods in this study, soil-pile force is identified.
After two experimental validations are conducted to confirm the applicability of the proposed input force identification methods. The identification results are compared and the different among each method are discussed.
Subjects
Input estimation
system identification
Kalman filter
strain measure
recursive least-squares estimator
Type
thesis
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