The Development of an Enhanced Mode Indicator Function Parameter Estimation Algorithm

1997 ◽  
Author(s):  
William A. Fladung ◽  
Allyn W. Phillips ◽  
David L. Brown
Keyword(s):  
Parameter Estimation ◽  
Estimation Procedure ◽  
Indicator Function ◽  
Degree Of Freedom ◽  
Function Parameter ◽  
Single Degree Of Freedom ◽  
Complex Mode ◽  
Single Degree ◽  
Parameter Estimation Procedure ◽  
Multiple Reference

Abstract The renewed interest in Multiple Reference Impact Testing has triggered the development of an Enhanced Complex Mode Indicator Function (EMIF) parameter estimation procedure. The EMIF method is a multiple degree of freedom extension of the Complex Mode Indicator Function (CMIF) method which is a simple but popular multiple reference parameter estimation procedure. The CMIF procedure uses a Enhanced Frequency Response Function (EFRF) which for many multiple reference systems looks like a single degree of freedom system over a narrow frequency band around a selected system eigenvalue. A single degree of freedom algorithm is used to obtain the system eigenvalue at the enhanced frequency. The EMIF method has been developed to handle those cases where the Single degree of freedom method is not valid.

THE REALIZATION OF THE ITERATIVE METHOD OF THE LEAST SQUARES FOR THE ESTIMATION OF STATIC OBJECT PARAMETERS IN MATLAB ENVIRONMENT

2017 ◽  
pp. 28-36
Author(s):  
Galina Vasil’evna Troshina ◽  
Alexander Aleksandrovich Voevoda
Keyword(s):  
Parameter Estimation ◽  
Iterative Method ◽  
Least Squares ◽  
Input Signal ◽  
Layer Structure ◽  
Estimation Procedure ◽  
Parameter Estimation Procedure ◽  
Noise Simulation ◽  
Static Object ◽  
Object Parameters

It was suggested to use the system model working in real time for an iterative method of the parameter estimation. It gives the chance to select a suitable input signal, and also to carry out the setup of the object parameters. The object modeling for a case when the system isn't affected by the measurement noises, and also for a case when an object is under the gaussian noise was executed in the MatLab environment. The superposition of two meanders with different periods and single amplitude is used as an input signal. The model represents the three-layer structure in the MatLab environment. On the most upper layer there are units corresponding to the simulation of an input signal, directly the object, the unit of the noise simulation and the unit for the parameter estimation. The second and the third layers correspond to the simulation of the iterative method of the least squares. The diagrams of the input and the output signals in the absence of noise and in the presence of noise are shown. The results of parameter estimation of a static object are given. According to the results of modeling, the algorithm works well even in the presence of significant measurement noise. To verify the correctness of the work of an algorithm the auxiliary computations have been performed and the diagrams of the gain behavior amount which is used in the parameter estimation procedure have been constructed. The entry conditions which are necessary for the work of an iterative method of the least squares are specified. The understanding of this algorithm functioning principles is a basis for its subsequent use for the parameter estimation of the multi-channel dynamic objects.

Kinematic comparison of single degree-of-freedom robotic gait trainers

2021 ◽  
Vol 159 ◽  
pp. 104258
Author(s):  
Jeonghwan Lee ◽  
Lailu Li ◽  
Sung Yul Shin ◽  
Ashish D. Deshpande ◽  
James Sulzer
Keyword(s):  
Degree Of Freedom ◽  
Single Degree Of Freedom ◽  
Single Degree ◽  
Robotic Gait
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