An Orthogonal View of the Polyreference Least-Squares Complex Frequency Modal Parameter Estimation Algorithm

2015 ◽  
pp. 171-182 ◽  
Author(s):  
William Fladung ◽  
Håvard Vold
Keyword(s):  
Parameter Estimation ◽  
Least Squares ◽  
Estimation Algorithm ◽  
Complex Frequency ◽  
Modal Parameter ◽  
Parameter Estimation Algorithm ◽  
Modal Parameter Estimation

Comparison of Modal Parameter Estimation Techniques for Electromechanical Rotordynamics of Cage Induction Motors

2007 ◽  
Author(s):  
Timo P. Holopainen ◽  
Seppo A. Aatola ◽  
C. Hunter Cloud ◽  
Guoxin Li
Keyword(s):  
Parameter Estimation ◽  
Least Squares ◽  
Frequency Domain ◽  
Theoretical Models ◽  
Complex Frequency ◽  
Modal Parameter ◽  
Electromagnetic System ◽  
Estimation Techniques ◽  
Modal Parameter Estimation ◽  
Parameter Estimation Techniques

Electromagnetic fields in the air gap of an electric motor produce electromagnetic forces between the rotor and stator. These forces couple the electromagnetic system to the mechanical one. This electromechanical interaction changes the vibration behaviour of the machine, and it may decrease the critical speeds, induce additional damping or cause rotordynamic instability. The experimental validation of theoretical models of these effects requires modal parameter estimation techniques which are reliable and robust. The main aim of this paper is to compare available techniques for the modal parameter estimation. The studied methods were: a) peak picking, b) prediction error, c) polyreference least-squares complex frequency-domain, d) multiple output backward autoregression, and e) polyreference least-squares complex exponential. Experimental data for the comparison was obtained using a standard six-pole 18 kW induction motor equipped with a long flexible shaft. In addition, the theoretical values using a simple electromechanical rotor model were calculated for the test cases. Comparison showed the short-comings of traditional frequency domain techniques and some advantages of the modern frequency- and time-domain techniques.

scholarly journals Least-Squares Parameter Estimation Algorithm for a Class of Input Nonlinear Systems

2012 ◽  
Vol 2012 ◽  
pp. 1-14 ◽  
Author(s):  
Weili Xiong ◽  
Wei Fan ◽  
Rui Ding
Keyword(s):  
Parameter Estimation ◽  
Nonlinear Systems ◽  
Least Squares ◽  
Moving Average ◽  
Estimation Algorithm ◽  
Autoregressive Moving Average ◽  
Parameter Estimates ◽  
Persistent Excitation ◽  
Estimation Algorithms ◽  
Parameter Estimation Algorithm

This paper studies least-squares parameter estimation algorithms for input nonlinear systems, including the input nonlinear controlled autoregressive (IN-CAR) model and the input nonlinear controlled autoregressive autoregressive moving average (IN-CARARMA) model. The basic idea is to obtain linear-in-parameters models by overparameterizing such nonlinear systems and to use the least-squares algorithm to estimate the unknown parameter vectors. It is proved that the parameter estimates consistently converge to their true values under the persistent excitation condition. A simulation example is provided.

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