Aircraft and Rotorcraft System Identification, Engineering Methods with Flight-Test Examples, 2nd Edition

2013 ◽  
Vol 58 (1) ◽  
pp. 1-2 ◽  
Author(s):  
Peter G. Hamel
Keyword(s):  
System Identification ◽  
Flight Test

System Identification Methods for Improving Flutter Flight Test Techniques

2004 ◽  
Author(s):  
David Klyde ◽  
Chuck Harris ◽  
Peter M. Thompson ◽  
Edward N. Bachelder
Keyword(s):  
System Identification ◽  
Flight Test ◽  
Identification Methods

scholarly journals Method for designing multi-input system identification signals using a compact time-frequency representation

2021 ◽  
Author(s):  
Mathias Stefan Roeser ◽  
Nicolas Fezans
Keyword(s):  
System Identification ◽  
Design Method ◽  
Flight Test ◽  
Compact Representation ◽  
Time Frequency ◽  
Stability And Control ◽  
Frequency Representation ◽  
Aerodynamic Parameters ◽  
Definition Of ◽  
And Control

AbstractA flight test campaign for system identification is a costly and time-consuming task. Models derived from wind tunnel experiments and CFD calculations must be validated and/or updated with flight data to match the real aircraft stability and control characteristics. Classical maneuvers for system identification are mostly one-surface-at-a-time inputs and need to be performed several times at each flight condition. Various methods for defining very rich multi-axis maneuvers, for instance based on multisine/sum of sines signals, already exist. A new design method based on the wavelet transform allowing the definition of multi-axis inputs in the time-frequency domain has been developed. The compact representation chosen allows the user to define fairly complex maneuvers with very few parameters. This method is demonstrated using simulated flight test data from a high-quality Airbus A320 dynamic model. System identification is then performed with this data, and the results show that aerodynamic parameters can still be accurately estimated from these fairly simple multi-axis maneuvers.

scholarly journals Reduced order system identification for UAVs

2015 ◽  
Vol 119 (1218) ◽  
pp. 961-980 ◽  
Author(s):  
P-D. Jameson ◽  
A. K. Cooke
Keyword(s):  
System Identification ◽  
Equations Of Motion ◽  
Real Data ◽  
Flight Test ◽  
Rate Of Change ◽  
Order System ◽  
Parameter Estimates ◽  
Reduced Order Models ◽  
Test Scenario ◽  
Reduced Order

Abstract Reduced order models representing the dynamic behaviour of symmetric aircraft are well known and can be easily derived from the standard equations of motion. In flight testing, accurate measurements of the dependent variables which describe the linearised reduced order models for a particular flight condition are vital for successful system identification. However, not all the desired measurements such as the rate of change in vertical velocity (Ẇ) can be accurately measured in practice. In order to determine such variables two possible solutions exist: reconstruction or differentiation. This paper addresses the effect of both methods on the reliability of the parameter estimates. The methods are used in the estimation of the aerodynamic derivatives for the Aerosonde UAV from a recreated flight test scenario in Simulink. Subsequently, the methods are then applied and compared using real data obtained from flight tests of the Cranfield University Jetstream 31 (G-NFLA) research aircraft.

System identification & attitude control of avian-type flyer with flight test

2017 ◽  
Author(s):  
Jangjin Oh ◽  
Seongyoung Kim ◽  
Byoungju Lee ◽  
Seungkeun Kim ◽  
Jinyoung Suk
Keyword(s):  
System Identification ◽  
Attitude Control ◽  
Flight Test

System Identification Modeling and Flight Characteristics Analysis of Miniature Coaxial Helicopter

2014 ◽  
Vol 59 (4) ◽  
pp. 36-51 ◽  
Author(s):  
Navid Dadkhah ◽  
Bérénice Mettler
Keyword(s):  
System Identification ◽  
High Speed ◽  
Tracking System ◽  
Flight Test ◽  
Lumped Parameter Model ◽  
Test Facility ◽  
Modeling And Analysis ◽  
Characteristics Analysis ◽  
Parameterized Model ◽  
Coaxial Helicopter

This paper describes the identification modeling and analysis of a miniature coaxial helicopter. The first part of the paper focuses on the development of the parameterized model with an emphasis on the coaxial rotor configuration. The model explicitly accounts for the dynamics of the lower rotor and uses an implicit lumped parameter model for the upper rotor and stabilizer bar. The parameterized model was identified using frequency domain system identification. The flight data collection experiments were performed in an indoor flight-test facility built around a commercial vision-based tracking system. The second part of the paper focuses on the verification of the model's accuracy, the consistency of the identified parameters, and the analysis of the flight dynamics. The accuracy was verified by comparing model-predicted responses with flight experimental responses. The identified parameters and model's physical consistency were examined using experiments in which specific aspects of the dynamics were isolated. For example, we used video images from a high-speed camera to verify the rotor and stabilizer bar time constants. Finally, the identified derivatives were verified based on first principles to demonstrate that the derivatives are physically meaningful.

Bell 412 System Identification and Model Fidelity Assessment for Hover and Forward Flight

2021 ◽  
Vol 66 (1) ◽  
pp. 1-13
Author(s):  
Seher-Weiß ◽  
Mark B. Tischler ◽  
Pavle Scepanovic ◽  
Arthur Gubbels
Keyword(s):  
System Identification ◽  
Rigid Body ◽  
Degrees Of Freedom ◽  
Flight Test ◽  
Vertical Axis ◽  
Forward Flight ◽  
Engine Model ◽  
Model Following Control ◽  
Fidelity Assessment ◽  
Six Dof

Frequency domain system identification of higher order models for the Bell 412 helicopter was performed. First, a frequency response database was derived from flight-test data. For hover, a combination of sweep and 2311-multistep maneuvers had to be used to achieve good results. In addition to the classical six-DoF (degrees of freedom) rigid body states, the identified hover model includes dynamic inflow, rotor coning dynamics, and uses a Padé approximation for the influence of engine dynamics, to improve the response in the vertical axis. The forward flight (60 kn) model includes as extension first-order flapping dynamics, mainly to improve the roll and pitch response. Besides the simple Padé approach used in the hover model, two different engine model structures were investigated but they provided no significant improvement compared to the Padé solution when coupled to the rigid-body model. Finally, a method derived from feedforward principles of model following control is shown, to use the identified hover model to analytically derive an "input filter" correction that improves the fidelity of a linearized FLIGHTLAB simulation model.

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