RUAV System Identification and Verification Using a Frequency-Domain Methodology

2014 ◽  
pp. 131-149
Author(s):  
I. Sánchez ◽  
D. Santamaría ◽  
A. Viguria ◽  
Aníbal Ollero ◽  
Guillermo Heredia
Keyword(s):  
System Identification ◽  
Frequency Domain

Frequency-Domain System Identification Techniques for Experimental and Operational Modal Analysis

2003 ◽  
Vol 36 (16) ◽  
pp. 1609-1614 ◽  
Author(s):  
Patrick Guillaume ◽  
Peter Verboven ◽  
Bart Cauberghe ◽  
Steve Vanlanduit ◽  
Eli Parloo ◽  
...  
Keyword(s):  
System Identification ◽  
Modal Analysis ◽  
Frequency Domain ◽  
Operational Modal Analysis ◽  
Identification Techniques

scholarly journals System identification-based frequency domain feature extraction for defect detection and characterization

2018 ◽  
Vol 98 ◽  
pp. 70-79 ◽  
Author(s):  
Ping Li ◽  
Zi–Qiang Lang ◽  
Ling Zhao ◽  
GuiYun Tian ◽  
Jeffrey A. Neasham ◽  
...  
Keyword(s):  
Feature Extraction ◽  
System Identification ◽  
Frequency Domain ◽  
Defect Detection

Fourier Series-Based Neural Networks for Vehicle System Identification

1999 ◽  
Author(s):  
Imtiaz Haque ◽  
Juergen Schuller
Keyword(s):  
Neural Networks ◽  
Fourier Series ◽  
System Identification ◽  
Transfer Function ◽  
Parameter Identification ◽  
Frequency Domain ◽  
Vehicle System ◽  
Non Linear ◽  
Function Identification ◽  
Non Linear System

Abstract The use of neural networks in system identification is an emerging field. Neural networks have become popular in recent years as a means to identify linear and non-linear systems whose characteristics are unknown. The success of sigmoidal networks in parameter identification has been limited. However, harmonic activation-based neural networks, recent arrivals in the field of neural networks, have shown excellent promise in linear and non-linear system parameter identification. They have been shown to have excellent generalization capability, computational parallelism, absence of local minima, and good convergence properties. They can be used in the time and frequency domain. This paper presents the application of a special class of such networks, namely Fourier Series neural networks (FSNN) to vehicle system identification. In this paper, the applications of the FSNNs are limited to the frequency domain. Two examples are presented. The results of the identification are based on simulation data. The first example demonstrates the transfer function identification of a two-degree-of freedom lateral dynamics model of an automobile. The second example involves transfer function identification for a quarter car model. The network set-up for such identification is described. The results of the network identification are compared with theory. The results indicate excellent prediction properties of such networks.

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