scholarly journals Comparison of Nonlinear System Identification Methods for Free Decay Measurements with Application to MEMS Devices

2017 ◽  
pp. 29-46 ◽  
Author(s):  
Vaclav Ondra ◽  
Robin Riethmueller ◽  
Matthew R. W. Brake ◽  
Christoph W. Schwingshackl ◽  
Pavel M. Polunin ◽  
...  
Keyword(s):  
System Identification ◽  
Nonlinear System ◽  
Nonlinear System Identification ◽  
Mems Devices ◽  
Identification Methods ◽  
Free Decay

scholarly journals Nonlinear System Identification Using Quasi-ARX RBFN Models with a Parameter-Classified Scheme

Complexity ◽  
2017 ◽  
Vol 2017 ◽  
pp. 1-12 ◽  
Author(s):  
Lan Wang ◽  
Yu Cheng ◽  
Jinglu Hu ◽  
Jinling Liang ◽  
Abdullah M. Dobaie
Keyword(s):  
System Identification ◽  
Nonlinear System ◽  
Radial Basis Function Network ◽  
Nonlinear System Identification ◽  
Support Vector ◽  
Identification Procedure ◽  
Identification Methods ◽  
Nonlinear Parameter Identification ◽  
And Control ◽  
Function Network

Quasi-linear autoregressive with exogenous inputs (Quasi-ARX) models have received considerable attention for their usefulness in nonlinear system identification and control. In this paper, identification methods of quasi-ARX type models are reviewed and categorized in three main groups, and a two-step learning approach is proposed as an extension of the parameter-classified methods to identify the quasi-ARX radial basis function network (RBFN) model. Firstly, a clustering method is utilized to provide statistical properties of the dataset for determining the parameters nonlinear to the model, which are interpreted meaningfully in the sense of interpolation parameters of a local linear model. Secondly, support vector regression is used to estimate the parameters linear to the model; meanwhile, an explicit kernel mapping is given in terms of the nonlinear parameter identification procedure, in which the model is transformed from the nonlinear-in-nature to the linear-in-parameter. Numerical and real cases are carried out finally to demonstrate the effectiveness and generalization ability of the proposed method.

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 Free-Decay Nonlinear System Identification via Mass-Change Scheme

2019 ◽  
Vol 2019 ◽  
pp. 1-14
Author(s):  
Dario Anastasio ◽  
Stefano Marchesiello
Keyword(s):  
System Identification ◽  
Nonlinear System ◽  
Nonlinear System Identification ◽  
State Space Model ◽  
Mass Change ◽  
Subspace Identification ◽  
Free Decay ◽  
Nonlinear Mechanical ◽  
Output Only ◽  
Nonlinear State

Methods for nonlinear system identification of structures generally require input-output measured data to estimate the nonlinear model, as a consequence of the noninvariance of the FRFs in nonlinear systems. However, providing a continuous forcing input to the structure may be difficult or impracticable in some situations, while it may be much easier to only measure the output. This paper deals with the identification of nonlinear mechanical vibrations using output-only free-decay data. The presented method is based on the nonlinear subspace identification (NSI) technique combined with a mass-change scheme, in order to extract both the nonlinear state-space model and the underlying linear system. The technique is tested first on a numerical nonlinear system and subsequently on experimental measurements of a multi-degree-of-freedom system comprising a localized nonlinearity.

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