System identification of a galvano scanner using input-output data obtained from positioning control

2015 ◽  
Author(s):  
Hirofumi Sekine ◽  
Shinji Ueda ◽  
Masayasu Suzuki ◽  
Mitsuo Hirata
Keyword(s):  
System Identification ◽  
Input Output ◽  
Output Data ◽  
Positioning Control

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.

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.

scholarly journals Statistical Inference for Piecewise Affine System Identification

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Hong Jianwang ◽  
Ricardo A. Ramirez-Mendoza ◽  
Xiang Yan
Keyword(s):  
System Identification ◽  
Unknown Parameter ◽  
Recursive Least Squares ◽  
Parameter Vector ◽  
Input Output ◽  
Output Data ◽  
Piecewise Affine ◽  
Affine System ◽  
Piecewise Affine System ◽  
Unknown Parameter Vector

This short note studies the problem of piecewise affine system identification, being a special nonlinear system based on our previous contribution on it. Two different identification strategies are proposed to achieve our mission, such as centralized identification and distributed identification. More specifically, for centralized identification, the total observed input-output data are used to estimate all unknown parameter vectors simultaneously without any consideration on the classification process. But for distributed identification, after the whole observed input-output data are classified into their own right subregions, then part input-output data, belonging to the same subregion, are applied to estimate the unknown parameter vector. Whatever the centralized identification and distributed identification, the final decision is to determine the unknown parameter vector in one linear form, so the recursive least squares algorithm and its modified form with the dead zone are studied to deal with the statistical noise and bounded noise, respectively. Finally, one simulation example is used to compare the identification accuracy for our considered two identification strategies.

Sign in / Sign up

Export Citation Format

Share Document