Non-parametric identification method of GFRFs for MIMO nonlinear system excited by multitone signal

2013 ◽  
Vol 332 (10) ◽  
pp. 2562-2574 ◽  
Author(s):  
Hai-tao Han ◽  
Hong-guang Ma ◽  
Dong-hui Xu ◽  
Zong-wei Wu ◽  
Dong-dong Yang ◽  
...  
Keyword(s):  
Nonlinear System ◽  
Parametric Identification ◽  
Identification Method ◽  
Mimo Nonlinear System ◽  
Non Parametric

Non-Parametric Identification Method of Volterra Kernels for Nonlinear Systems Excited by Multitone Signal

2013 ◽  
Vol 16 (2) ◽  
pp. 519-529 ◽  
Author(s):  
H. T. Han ◽  
H. G. Ma ◽  
L. N. Tan ◽  
J. F. Cao ◽  
J. L. Zhang
Keyword(s):  
Nonlinear Systems ◽  
Parametric Identification ◽  
Volterra Kernels ◽  
Identification Method ◽  
Non Parametric

scholarly journals Parametric Identification of Nonlinear Vibration Systems Via Polynomial Chirplet Transform

2016 ◽  
Vol 138 (5) ◽  
Author(s):  
Y. Deng ◽  
C. M. Cheng ◽  
Y. Yang ◽  
Z. K. Peng ◽  
W. X. Yang ◽  
...  
Keyword(s):  
Nonlinear System ◽  
Physical Property ◽  
Parametric Identification ◽  
Parameters Estimation ◽  
Instantaneous Amplitude ◽  
Nonlinear Identification ◽  
Nonstationary Signals ◽  
Identification Method ◽  
Chirplet Transform ◽  
System Properties

The response of a nonlinear oscillator is characterized by its instantaneous amplitude (IA) and instantaneous frequency (IF) features, which can be significantly affected by the physical properties of the system. Accordingly, the system properties could be inferred from the IA and IF of its response if both instantaneous features can be identified accurately. To fulfill such an idea, a nonlinear system parameter identification method is proposed in this paper with the aid of polynomial chirplet transform (PCT), which has been proved a powerful tool for processing nonstationary signals. First, the PCT is used to extract the instantaneous characteristics, i.e., IA and IF, from nonlinear system responses. Second, instantaneous modal parameters estimation was adopted to extract backbone and damping curves, which characterize the inherent nonlinearities of the system. Third, the physical property parameters of the system were estimated through fitting the identified average nonlinear characteristic curves. Finally, the proposed nonlinear identification method is experimentally validated through comparing with two Hilbert transform (HT) based methods.

scholarly journals Non-parametric identification of thermal control system elements

2021 ◽  
pp. 7-20
Author(s):  
Vasilisa Boeva ◽  
◽  
Yuri Voskoboinikov ◽  
Rustam Mansurov ◽  
◽  
...  
Keyword(s):  
Integral Equation ◽  
Control System ◽  
Impulse Response ◽  
Stable Solution ◽  
Parametric Identification ◽  
Thermal Control ◽  
Black Box ◽  
Response Evaluation ◽  
Thermal Control System ◽  
Non Parametric

The thermal control system “Heater-Fan-Room” is represented by three different-type interconnected simpler subsystems. In this paper, a “black-box” whose structure is not specified is used as a mathematical model of the system and subsystems due to complexity of physical processes proceeding in these subsystems. For stationary linear systems, the connection between an input and an output of the “black-box” is defined by the Volterra integral equation of the first kind with an undetermined difference kernel also known as impulse response in the automatic control theory. In such a case, it is necessary to evaluate an unknown impulse response to use the “black-box” model and formulate all subsystems and the system as a whole. This condition complicates significantly the solution search of non-parametric identification problems in the system because an output of one subsystem is an input of another subsystem, so active identification schemes are unappropriated. Formally, an impulse response evaluation is a solution of the integral equation of the first kind for its kernel by registered noise-contaminated discrete input and output values. This problem is ill-posed because of the possible solution instability (impulse response evaluation in this case) relative to measurement noises in initial data. To find a unique stable solution regularizing algorithms are used, but the specificity of the impulse response identification experiment in the “Heater-Fan-Room” system do not allow applying computational methods of these algorithms (a system of linear equations or discrete Fourier transformation). In this paper, the authors propose two specific identification algorithms for complex technical systems. In these algorithms, impulse responses are evaluated using first derivatives of identified system signals that are stably calculated by smoothing cubic splines with an original smoothing parameter algorithm. The results of the complex “Heater-Fan-Room” system modeling and identification prove the efficiency of the algorithms proposed. Acknowledgments: The reported study was funded by RFBR, project number 20-38-90041.

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