System identification with noisy input-output data using a cumulant-based Steiglitz-McBride algorithm

1997 ◽  
Vol 44 (5) ◽  
pp. 407-409 ◽  
Author(s):  
J.M.M. Anderson ◽  
W. Edmonson
Keyword(s):  
System Identification ◽  
Input Output ◽  
Output Data ◽  
Noisy Input

scholarly journals System Identification of a MEMS Gyroscope

1999 ◽  
Vol 123 (2) ◽  
pp. 201-210 ◽  
Author(s):  
Robert T. M’Closkey ◽  
Steve Gibson ◽  
Jason Hui
Keyword(s):  
System Identification ◽  
Angular Rate ◽  
Experimental System ◽  
Primary Objective ◽  
Input Output ◽  
Output Data ◽  
Mems Gyroscope ◽  
Principal Axes ◽  
Frequency Split ◽  
Insight Into

This paper reports the experimental system identification of the Jet Propulsion Laboratory MEMS vibratory rate gyroscope. A primary objective is to estimate the orientation of the stiffness matrix principal axes for important sensor dynamic modes with respect to the electrode pick-offs in the sensor. An adaptive lattice filter is initially used to identify a high-order two-input/two-output transfer function describing the input/output dynamics of the sensor. A three-mode model is then developed from the identified input/output model to determine the axes’ orientation. The identified model, which is extracted from only two seconds of input/output data, also yields the frequency split between the sensor’s modes that are exploited in detecting the rotation rate. The principal axes’ orientation and frequency split give direct insight into the source of quadrature measurement error that corrupts detection of the sensor’s angular rate.

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