Nonlinear Model for Sub- and Superharmonic Motions of a MDOF Moored Structure, Part 2—Sensitivity Analysis and Comparison

2005 ◽  
Vol 127 (4) ◽  
pp. 291-299
Author(s):  
S. C. S. Yim ◽  
S. Raman ◽  
P. A. Palo
Keyword(s):  
Sensitivity Analysis ◽  
System Identification ◽  
Nonlinear System ◽  
Nonlinear System Identification ◽  
Detailed Comparison ◽  
Identification Procedure ◽  
System Parameters ◽  
Nonlinear Responses ◽  
Identification Technique ◽  
Single Set

The nonlinear R-MI/SO system identification procedure and the parameters of the MDOF system identified in Part 1 are examined in detail in this paper. A parametric study is conducted and the results are presented on the sensitivity of the system parameters for two key nonlinear responses—subharmonic and superharmonic resonances. The parameters are compared to determine the appropriateness of using a single set of system parameters for both response regions. A detailed comparison of the MDOF and the corresponding SDOF system results is performed. The knowledge gained from the SDOF and MDOF studies on the applicability of the R-MISO technique for the system identification of MDOF submerged moored structures is discussed. The results show that the MDOF extension of the R-MI/SO nonlinear system identification technique works well; the resulting system parameters are relatively constant and can be applied to the both the sub- and superharmonic regions.

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.

Identification of Instantaneous Frequency and Damping From Transient Decay Data

2020 ◽  
Vol 142 (5) ◽  
Author(s):  
Mengshi Jin ◽  
Wei Chen ◽  
Matthew R. W. Brake ◽  
Hanwen Song
Keyword(s):  
System Identification ◽  
Nonlinear System ◽  
Signal To Noise Ratio ◽  
Nonlinear System Identification ◽  
Uncertainty Assessment ◽  
Identification Procedure ◽  
Zero Crossing ◽  
Nonlinear Properties ◽  
Fitting Method ◽  
Comparison Results

Abstract Jointed interfaces, damage, wear, or non-idealized boundary conditions often introduce nonlinear characteristics to assembled structures. Consequently, extensive research has been carried out regarding nonlinear system identification. The development of nonlinear system identification is also enabling the intentional application of nonlinearities towards practical fields such as vibration control and energy harvesting. This research proposes a nonlinear identification procedure that consists of two steps: first, the raw data is filtered by the Double Reverse Multimodal Decomposition method that involves system reconstruction, expansion, and filtering twice. Second, the Peak Finding and Fitting method is applied to the filtered signal to extract the instantaneous amplitude and frequency. The identification procedure is applied to the measured responses from a jointed structure to assess its efficacy. The results are compared with those obtained from other well-known methods—the Hilbert transform and zero-crossing methods. The comparison results indicate that the Peaking Finding and Fitting method extracts the amplitude of the response signal more accurately. Consequently, this yields a higher signal-to-noise ratio in the extracted damping values. As a recommended last step, uncertainty assessment is conducted to calculate the 95% confidence intervals of the nonlinear properties of the system.

A Technique for Estimating Linear Parameters Using Nonlinear Restoring Force Extraction in the Absence of an Input Measurement

2005 ◽  
Vol 127 (5) ◽  
pp. 483-492 ◽  
Author(s):  
Muhammad Haroon ◽  
Douglas E. Adams ◽  
Yiu Wah Luk
Keyword(s):  
System Identification ◽  
Nonlinear System ◽  
Nonlinear System Identification ◽  
Restoring Force ◽  
Nonlinear Parameters ◽  
System Parameters ◽  
System Data ◽  
Vehicle Suspension System ◽  
Two Stages ◽  
Parameter Estimation Techniques

Conventional nonlinear system identification procedures estimate the system parameters in two stages. First, the nominally linear system parameters are estimated by exciting the system at an amplitude (usually low) where the behavior is nominally linear. Second, the nominally linear parameters are used to estimate the nonlinear parameters of the system at other arbitrary amplitudes. This approach is not suitable for many mechanical systems, which are not nominally linear over a broad frequency range for any operating amplitude. A method for nonlinear system identification, in the absence of an input measurement, is presented that uses information about the nonlinear elements of the system to estimate the underlying linear parameters. Restoring force, boundary perturbation, and direct parameter estimation techniques are combined to develop this approach. The approach is applied to experimental tire-vehicle suspension system data.

scholarly journals Comparing system identification techniques for identifying human-like walking controllers

2021 ◽  
Vol 8 (12) ◽  
Author(s):  
Dave Schmitthenner ◽  
Anne E. Martin
Keyword(s):  
System Identification ◽  
Nonlinear System ◽  
Linear System ◽  
Nonlinear System Identification ◽  
Ordinary Least Squares ◽  
Human Gait ◽  
Slip Model ◽  
Identification Technique ◽  
Experimental Values ◽  
Identification Techniques

While human walking has been well studied, the exact controller is unknown. This paper used human experimental walking data and system identification techniques to infer a human-like controller for a spring-loaded inverted pendulum (SLIP) model. Because the best system identification technique is unknown, three methods were used and compared. First, a linear system was found using ordinary least squares. A second linear system was found that both encoded the linearized SLIP model and matched the first linear system as closely as possible. A third nonlinear system used sparse identification of nonlinear dynamics (SINDY). When directly mapping states from the start to the end of a step, all three methods were accurate, with errors below 10% of the mean experimental values in most cases. When using the controllers in simulation, the errors were significantly higher but remained below 10% for all but one state. Thus, all three system identification methods generated accurate system models. Somewhat surprisingly, the linearized system was the most accurate, followed closely by SINDY. This suggests that nonlinear system identification techniques are not needed when finding a discrete human gait controller, at least for unperturbed walking. It may also suggest that human control of normal, unperturbed walking is approximately linear.

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