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.

The Parameters Identification of Airplane Flutter Model Based Global Nonlinear Separable Least Square Method

2012 ◽  
Vol 490-495 ◽  
pp. 2774-2779
Author(s):  
Jie Yao ◽  
Jian Hong Wang
Keyword(s):  
Transfer Function ◽  
Least Square Method ◽  
Least Square ◽  
Model Parameters ◽  
Transfer Function Model ◽  
Input Output ◽  
Function Model ◽  
Output Data ◽  
Noisy Input ◽  
Least Square Problem

considering the noisy input-output data, this paper come up with an idea extending the deviation compensation least square (CLS)to the nonlinear separable least square (NSLS).The least square method employed nonlinear separable least square adaptive to the noisy situation is able to identify accurately the airplane flutter model parameters. Combining the transfer function model, this algorithm successfully converts the identification of noisy system into nonlinear separable least square problem. The square deviation of two noises and the model parameter of the transfer function can be estimated separately by using of above algorithm.

Dynamic System Identification Using a Step Input

2005 ◽  
Vol 24 (2) ◽  
pp. 125-134
Author(s):  
Manabu Kosaka ◽  
Hiroshi Uda ◽  
Eiichi Bamba ◽  
Hiroshi Shibata
Keyword(s):  
Steady State ◽  
System Identification ◽  
Transfer Function ◽  
Step Response ◽  
Output Data ◽  
Mass System ◽  
Input And Output ◽  
Rational Transfer ◽  
Line Identification ◽  
The Moment

In this paper, we propose a deterministic off-line identification method performed by using input and output data with a constant steady state output response such as a step response that causes noise or vibration from a mechanical system at the moment when it is applied but they are attenuated asymptotically. The method can directly acquire any order of reduced model without knowing the real order of a plant, in such a way that the intermediate parameters are uniquely determined so as to be orthogonal with respect to 0 ∼ N-tuple integral values of output error and irrelevant to the unmodelled dynamics. From the intermediate parameters, the coefficients of a rational transfer function are calculated. In consequence, the method can be executed for any plant without knowing or estimating its order at the beginning. The effectiveness of the method is illustrated by numerical simulations and also by applying it to a 2-mass system.

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 Nonlinear System Identification in coherence with Nonlinearity measure for Dynamic Physical systems- Case Studies

2021 ◽  
Author(s):  
Joanofarc Xavier ◽  
Rames C Panda ◽  
SK Patnaik
Keyword(s):  
Nonlinear Dynamics ◽  
System Identification ◽  
Nonlinear System ◽  
Case Studies ◽  
Nonlinear System Identification ◽  
Least Square ◽  
Measurement Tool ◽  
Identification Methods ◽  
Physical Systems ◽  
Highly Nonlinear

Abstract With the recent success of using the time series to vast applications, one would expect its boundless adaptation to problems like nonlinear control and nonlinearity quantification. Though there exist many system identification methods, finding suitable method for identifying a given process is still cryptic. Moreover, to this notch, research on their usage to nonlinear system identification and classification of nonlinearity remains limited. This article hovers around the central idea of developing a ‘kSINDYc’ (key term based Sparse Identification of Nonlinear Dynamics with control) algorithm to capture the nonlinear dynamics of typical physical systems. Furthermore, existing two reliable identification methods namely NL2SQ (Nonlinear least square method) and N3ARX (Neural network based nonlinear auto regressive exogenous input scheme) are also considered for all the physical process-case studies. The primary spotlight of present research is to encapsulate the nonlinear dynamics identified for any process with its nonlinearity level through a mathematical measurement tool. The nonlinear metric Convergence Area based Nonlinear Measure (CANM) calculates the process nonlinearity in the dynamic physical systems and classifies them under mild, medium and highly nonlinear categories. Simulation studies are carried-out on five industrial systems with divergent nonlinear dynamics. The user can make a flawless choice of specific identification methods suitable for given process by finding the nonlinear metric (Δ0). Finally, parametric sensitivity on the measurement has been studied on CSTR and Bioreactor to evaluate the efficacy of kSINDYc on system identification.

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.

Damage Identification Using Sensitivity-Enhancing Control and Identified Models

2005 ◽  
Vol 128 (2) ◽  
pp. 210-220 ◽  
Author(s):  
Jason A. Solbeck ◽  
Laura R. Ray
Keyword(s):  
Transfer Function ◽  
Closed Loop ◽  
Damage Identification ◽  
Structural Models ◽  
Damage Parameter ◽  
Measured Parameter ◽  
Parameter Vector ◽  
Input Output ◽  
Output Data ◽  
Torsional System

This paper investigates a coherence approach for locating structural damage using modal frequencies and transfer function parameters identified from input-output data using Observer/Kalman filter identification (OKID). Autonomous damage identification using such forward methods generally require (i) a structural model by which to relate measured and predicted modal properties induced by damage, and (ii) good sensitivity of modal parameter changes to damage states. Using the coherence approach, a damage parameter vector comprised of a finite set of modal frequencies and transfer function parameters is hypothesized for each damage case using either identified or analytic structural models. Measured parameter vectors are extracted from experimental input-output data for a damaged structure using OKID and are compared to hypotheses to determine the most likely damage state. The richness of the parameter vector set, which is comprised of high-quality frequency measurements and lower-quality transfer function parameters, is evaluated in order to determine the ability to uniquely localize damage. The method is evaluated experimentally using a three-degree-of-freedom torsional system and a space-frame truss. Damage parameter hypotheses are generated from a model of the healthy structure developed by system identification in the torsional system, and an analytic model is used to generate damage hypotheses for the truss structure. Feedback control laws enhance the parameter vectors by including closed-loop modal frequencies in order to reduce noise sensitivity and improve uniqueness of parameter vector hypotheses to each damage case. Results show improvements in damage identification using damage parameter vectors comprised of open- and closed-loop modal frequencies, even when model error exists in structural models used to form damage parameter vector hypotheses.

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