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.

scholarly journals Non-invasive methods applied to the case of Municipal Solid Waste landfills (MSW): analysis of long-term data

2008 ◽  
Vol 19 ◽  
pp. 33-38 ◽  
Author(s):  
A. Scozzari
Keyword(s):  
System Identification ◽  
Meteorological Parameters ◽  
Flux Measurement ◽  
Input Output ◽  
Real World Data ◽  
Output Data ◽  
Non Invasive ◽  
Flux Monitoring ◽  
Municipal Solid Waste Landfills

Abstract. This work presents and discusses a methodology for modeling the behavior of a landfill system in terms of biogas release to the atmosphere, relating this quantity to local meteorological parameters. One of the most important goals in the study of MSW sites lies in the optimization of biogas collection, thus minimizing its release to the atmosphere. After an introductory part, that presents the context of non-invasive measurements for the assessment of biogas release, the concepts of survey mapping and automatic flux monitoring are introduced. Objective of this work is to make use of time series coming from long-term flux monitoring campaigns in order to assess the trend of gas release from the MSW site. A key aspect in processing such data is the modeling of the effect of meteorological parameters over such measurements; this is accomplished by modeling the system behavior with a set of Input/Output data to characterize it without prior knowledge (system identification). The system identification approach presented here is based on an adaptive simulation concept, where a set of Input/Output data help training a "black box" model, without necessarily a prior analytical knowledge. The adaptive concept is based on an Artificial Neural Network scheme, which is trained by real-world data coming from a long-term monitoring campaign; such data are also used to test the real forecasting capability of the model. In this particular framework, the technique presented in this paper appears to be very attractive for the evaluation of biogas releases on a long term basis, by simulating the effects of meteorological parameters over the flux measurement, thus enhancing the extraction of the useful information in terms of a gas "flux" quantity.

Mechanism Analysis and System Identification of the Quadrature Error in MEMS Gyroscope

2007 ◽  
Author(s):  
Jun Yang ◽  
ZhongYu Gao ◽  
Rong Zhang ◽  
ZhiYong Chen ◽  
Bin Zhou
Keyword(s):  
System Identification ◽  
Stiffness Matrix ◽  
Experimental System ◽  
Structure Parameters ◽  
Mechanism Analysis ◽  
Dynamics Model ◽  
Tsinghua University ◽  
Mems Gyroscope ◽  
Quadrature Error ◽  
Function Matrix

This paper discusses error mechanism and experimental system identification of the quadrature error present in Tsinghua University MEMS vibratory rate gyroscopes. The magnitude and stability of the quadrature error are both of concern because they corrupt the measurement of the actual input rate. The stiffness matrix of suspension beams and a three-mode dynamics model of gyroscope structure are developed and the analysis demonstrates that structure imperfections result in the quadrature error, which is generally considerably larger than Coriolis-induced rate signal. The gyroscope dynamics and structure parameters are identified by modal experiment and frequency response function matrix. The experiment results from twenty gyroscope samples demonstrate that structure imperfections are main reasons resulting in the quadrature bias of the gyroscope, including anis-elasticity of suspension beams, and stiffness coupling between drive axis and sense axis. The identified structure parameters will also contribute to future laser trimming for reducing the quadrature error of gyroscope.

scholarly journals System Identification of MEMS Vibratory Gyroscope Sensor

2011 ◽  
Vol 2011 ◽  
pp. 1-12 ◽  
Author(s):  
Juntao Fei ◽  
Yuzheng Yang
Keyword(s):  
System Identification ◽  
Direct Method ◽  
Angular Rate ◽  
Mems Gyroscope ◽  
System Parameters ◽  
Vibratory Gyroscope ◽  
True Value ◽  
Simulation Results ◽  
Fabrication Defects ◽  
Gyroscope Sensor

Fabrication defects and perturbations affect the behavior of a vibratory MEMS gyroscope sensor, which makes it difficult to measure the rotation angular rate. This paper presents a novel adaptive approach that can identify, in an online fashion, angular rate and other system parameters. The proposed approach develops an online identifier scheme, by rewriting the dynamic model of MEMS gyroscope sensor, that can update the estimator of angular rate adaptively and converge to its true value asymptotically. The feasibility of the proposed approach is analyzed and proved by Lyapunov's direct method. Simulation results show the validity and effectiveness of the online identifier.

scholarly journals Input-Output Data Scaling for System Identification

2006 ◽  
Vol 39 (1) ◽  
pp. 618-623 ◽  
Author(s):  
Seth L. Lacy ◽  
Vit Babuška
Keyword(s):  
System Identification ◽  
Input Output ◽  
Output Data ◽  
Data Scaling

Development of System Identification for Piezoelectric Patch Actuator

2015 ◽  
Vol 761 ◽  
pp. 245-249
Author(s):  
Mohd Nazmin Maslan ◽  
Z. Jamaludin ◽  
Muhamad Arfauz A. Rahman ◽  
Lokman Abdullah ◽  
Mohd Lutfan Abd Latib ◽  
...  
Keyword(s):  
System Identification ◽  
Transfer Function ◽  
Least Square ◽  
Input Output ◽  
Output Data ◽  
Piezoelectric Patch ◽  
Direct Measurements ◽  
Highly Nonlinear

This paper presents the development of the system identification (SI) for the highly nonlinear piezoelectric patch actuator. The transfer function is determined by using the nonlinear least square (NLS) method after the direct measurements of input-output data are taken from the actuator that is installed on a well-equipped platform. The results were validated to ensure that the transfer function derived fits well with the experimental output.

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