Experimental Nonlinear Model Identification of a Highly Nonlinear Resonator

2018 ◽  
Vol 140 (3) ◽  
Author(s):  
Tanju Yildirim ◽  
Jiawei Zhang ◽  
Shuaishuai Sun ◽  
Gursel Alici ◽  
Shiwu Zhang ◽  
...  

In this work, two model identification methods are used to estimate the nonlinear large deformation behavior of a nonlinear resonator in the time and frequency domains. A doubly clamped beam with a slender geometry carrying a central intraspan mass when subject to a transverse excitation is used as the highly nonlinear resonator. A nonlinear Duffing equation has been used to represent the system for which the main source of nonlinearity arises from large midplane stretching. The first model identification technique uses the free vibration of the system and the Hilbert transform (HT) to identify a nonlinear force–displacement relationship in the large deformation region. The second method uses the frequency response of the system at various base accelerations to relate the maximum resonance frequency to the nonlinear parameter arising from the centerline extensibility. Experiments were conducted using the doubly clamped slender beam and an electrodynamic shaker to identify the model parameters of the system using both of the identification techniques. It was found that both methods produced near identical model parameters; an excellent agreement between theory and experiments was obtained using either of the identification techniques. This follows that two different model identification techniques in the time and frequency domains can be employed to accurately predict the nonlinear response of a highly nonlinear resonator.

1998 ◽  
Vol 120 (2) ◽  
pp. 331-338 ◽  
Author(s):  
Y. Ren ◽  
C. F. Beards

Almost all real-life structures are assembled from components connected by various types of joints. Unlike many other parts, the dynamic properties of a joint are difficult to model analytically. An alternative approach for establishing a theoretical model of a joint is to extract the model parameters from experimental data using joint identification techniques. The accuracy of the identification is significantly affected by the properties of the joints themselves. If a joint is stiff, its properties are often difficult to identify accurately. This is because the responses at both ends of the joint are linearly-dependent. To make things worse, the existence of a stiff joint can also affect the accuracy of identification of other effective joints (the term “effective joints” in this paper refers to those joints which otherwise can be identified accurately). This problem is tackled by coupling these stiff joints using a generalized coupling technique, and then the properties of the remaining joints are identified using a joint identification technique. The accuracy of the joint identification can usually be improved by using this approach. Both numerically simulated and experimental results are presented.


2015 ◽  
Vol 77 (20) ◽  
Author(s):  
Abdulrahman A. A. Emhemed ◽  
Rosbi Mamat ◽  
Ahmad ‘Athif Mohd Faudzi

The aim of this paper is to present experimental, empirical and analytic identification techniques, known as non-parametric techniques. Poor dynamics and high nonlinearities are parts of the difficulties in the control of pneumatic actuator functions, which make the identification technique very challenging. Firstly, the step response experimental data is collected to obtain real-time force model of the intelligent pneumatic actuator (IPA). The IPA plant and Personal Computer (PC) communicate through Data Acquisition (DAQ) card over MATLAB software. The second method is approximating the process by curve reaction of a first-order plus delay process, and the third method uses the equivalent n order process with PTn model parameters. The obtained results have been compared with the previous study, achieved based on force system identification of IPA obtained by the (Auto-Regressive model with eXogenous) ARX model. The models developed using non-parameters identification techniques have good responses and their responses are close to the model identified using the ARX system identification model. The controller approved the success of the identification technique with good performance. This means the Non-Parametric techniques are strongly recommended, suitable, and feasible to use to analyze and design the force controller of IPA system. The techniques are thus very suitable to identify the real IPA plant and achieve widespread industrial acceptance.


2015 ◽  
Vol 135 (12) ◽  
pp. 1565-1573
Author(s):  
Yoshitaka Ohshio ◽  
Daisuke Ikefuji ◽  
Yuko Suhara ◽  
Masato Nakayama ◽  
Takanobu Nishiura

Author(s):  
Włodzimierz Pogribny ◽  
Marcin Drzycimski ◽  
Zdzisław Drzycimski

Author(s):  
Neophytos Chiras ◽  
Ceri Evans ◽  
David Rees

This paper examines the estimation of a global nonlinear gas turbine model using NARMAX techniques. Linear models estimated on small-signal data are first examined and the need for a global nonlinear model is established. A nonparametric analysis of the engine nonlinearity is then performed in the time and frequency domains. The information obtained from the linear modelling and nonlinear analysis is used to restrict the search space for nonlinear modelling. The nonlinear model is then validated using large-signal data and its superior performance illustrated by comparison with a linear model. This paper illustrates how periodic test signals, frequency domain analysis and identification techniques, and time-domain NARMAX modelling can be effectively combined to enhance the modelling of an aircraft gas turbine.


2021 ◽  
Vol 291 ◽  
pp. 116721
Author(s):  
Han Li ◽  
Zhe Wang ◽  
Tianzhen Hong ◽  
Andrew Parker ◽  
Monica Neukomm

1995 ◽  
Vol 381 (1-2) ◽  
pp. 1-4 ◽  
Author(s):  
S. Feliu ◽  
J.C. Galvan ◽  
S. Feliu ◽  
J. Simancas ◽  
J.M. Bastidas ◽  
...  

NeuroImage ◽  
2000 ◽  
Vol 11 (5) ◽  
pp. S714
Author(s):  
Claudio Babiloni ◽  
Filippo Carducci ◽  
Fabio Babiloni ◽  
Febo Cincotti ◽  
Fabrizio Rosciarelli ◽  
...  

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