Tyre Dynamics: Model Validation and Parameter Identification

2015 ◽  
pp. 219-232
Author(s):  
Andreas Hackl ◽  
Wolfgang Hirschberg ◽  
Cornelia Lex ◽  
Georg Rill
Keyword(s):  
Parameter Identification ◽  
Model Validation ◽  
Dynamics Model

scholarly journals Frequency-based model validation and parameter identification of a sea-surface vehicle

2009 ◽  
Vol 42 (13) ◽  
pp. 284-291
Author(s):  
Francesco Carravetta ◽  
Giovanni Felici ◽  
Pasquale Palumbo
Keyword(s):  
Parameter Identification ◽  
Model Validation ◽  
Sea Surface

scholarly journals Feature Extraction for Structural Dynamics Model Validation

2016 ◽  
Author(s):  
Charles Farrar ◽  
Mayuko Nishio ◽  
Francois Hemez ◽  
Chris Stull ◽  
Gyuhae Park ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Model Validation ◽  
Structural Dynamics ◽  
Dynamics Model

Model Parameter Identification of Nonlinear Dynamics Systems by Trajectory Pattern Method

2010 ◽  
Author(s):  
Jahangir Rastegar ◽  
Dake Feng ◽  
Kavous Jorabchi
Keyword(s):  
Nonlinear Dynamics ◽  
System Dynamics ◽  
Parameter Identification ◽  
System Stability ◽  
Model Parameters ◽  
System Dynamics Model ◽  
Dynamics Model ◽  
Higher Order Terms ◽  
Trajectory Pattern ◽  
Trajectory Pattern Method

In this paper, a new method is presented for model parameter identification of a large class of fully controlled nonlinear dynamics systems such as robot manipulators. The method uses trajectory patterns with feed-forward controls to identify model parameters of the system. The developed method ensures full system stability, does not require close initial estimated values for the parameters to be identified, and provides a systematic method of emphasizing on the estimation of the parameters associated with lower order terms of the system dynamics model and gradually upgrading the accuracy with which the model parameters, particularly those associated with the higher order terms of the system dynamics model are estimated. The developed method is based on Trajectory Pattern Method (TPM). In this method, for a pattern of motion the inverse dynamics model of the system is derived in algebraic form in terms of the trajectory pattern parameters. The system dynamics model parameters are then identified using a systematic algorithm which ensures system stability as well as accurate estimation of the model parameters associated with lower as well as higher order terms. Mathematical proof of convergence of the developed method and an example of its application are provided.

scholarly journals Parameter Identification and Model Validation of a Macroscopic Traffic Model

2017 ◽  
Vol 50 (1) ◽  
pp. 12835-12840 ◽  
Author(s):  
Elvira Thonhofer ◽  
Martin Fuhrmann ◽  
Stefan Jakubek
Keyword(s):  
Parameter Identification ◽  
Model Validation ◽  
Traffic Model ◽  
Macroscopic Traffic Model
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