A Ground-Motion Transfer Function Matrix between Two Nearby Rock and Soil Sites: A System Identification Problem

2011 ◽  
Vol 101 (1) ◽  
pp. 222-234 ◽  
Author(s):  
R. Benites ◽  
T. G. Caldwell
Keyword(s):  
System Identification ◽  
Transfer Function ◽  
Ground Motion ◽  
Identification Problem ◽  
Transfer Function Matrix ◽  
Rock And Soil ◽  
Function Matrix

System Identification Based on Recursive Least Square Method for the Magnetic Suspension Active Vibration Isolation System

2012 ◽  
Vol 150 ◽  
pp. 105-110 ◽  
Author(s):  
Bei Bei Yang ◽  
Ye Fa Hu ◽  
Guo Ping Ding
Keyword(s):  
System Identification ◽  
Transfer Function ◽  
Vibration Isolation ◽  
Isolation Effect ◽  
Isolation System ◽  
Vibration Isolation System ◽  
Transfer Function Matrix ◽  
Active Vibration ◽  
System Transfer Function ◽  
Function Matrix

In this paper, we use recursive least squares method for magnetic single layer vibration isolation system identification to get the system transfer function matrix. By considering the fitting degree, pole-zero, the step response to adjust the order of model and noise structure for optimizing the model Identification. Applying the system transfer function matrix to the magnetic active vibration control system to improve the isolation effect. The results showed that: significantly improved isolation effect, verify the validity of this identification model for magnetic single isolation system.

Error analyses of a Multi-Input-Multi-Output cantilever beam test

2021 ◽  
pp. 107754632110337
Author(s):  
Arup Maji ◽  
Fernando Moreu ◽  
James Woodall ◽  
Maimuna Hossain
Keyword(s):  
Transfer Function ◽  
Frequency Domain ◽  
Cantilever Beam ◽  
Transfer Functions ◽  
Linear Superposition ◽  
Transfer Function Matrix ◽  
Error Analyses ◽  
Pseudo Inverse ◽  
Function Matrix

Multi-Input-Multi-Output vibration testing typically requires the determination of inputs to achieve desired response at multiple locations. First, the responses due to each input are quantified in terms of complex transfer functions in the frequency domain. In this study, two Inputs and five Responses were used leading to a 5 × 2 transfer function matrix. Inputs corresponding to the desired Responses are then computed by inversion of the rectangular matrix using Pseudo-Inverse techniques that involve least-squared solutions. It is important to understand and quantify the various sources of errors in this process toward improved implementation of Multi-Input-Multi-Output testing. In this article, tests on a cantilever beam with two actuators (input controlled smart shakers) were used as Inputs while acceleration Responses were measured at five locations including the two input locations. Variation among tests was quantified including its impact on transfer functions across the relevant frequency domain. Accuracy of linear superposition of the influence of two actuators was quantified to investigate the influence of relative phase information. Finally, the accuracy of the Multi-Input-Multi-Output inversion process was investigated while varying the number of Responses from 2 (square transfer function matrix) to 5 (full-rectangular transfer function matrix). Results were examined in the context of the resonances and anti-resonances of the system as well as the ability of the actuators to provide actuation energy across the domain. Improved understanding of the sources of uncertainty from this study can be used for more complex Multi-Input-Multi-Output experiments.

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