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

Case Studies of System Identification Modeling for Flight Control Design

2013 ◽  
Vol 58 (1) ◽  
pp. 1-16 ◽  
Author(s):  
Christina M. Ivler ◽  
Mark B. Tischler
Keyword(s):  
System Identification ◽  
Flight Control ◽  
Control Design ◽  
Flight Test ◽  
Unmanned Aircraft ◽  
Integrated System ◽  
Control Law ◽  
Dynamics Model ◽  
Identification Methods ◽  
Flight Dynamics Model

Flight control design and analysis requires an accurate flight dynamics model of the bare airframe and its associated uncertainties, as well as the integrated system model (block diagrams), across the frequency range of interest. Frequency response system identification methods have proven to efficiently fulfill these modeling requirements in recent rotorcraft flight control applications. This paper presents integrated system identification methods for control law design with flight-test examples of the Fire Scout MQ-8B, S-76, and ARH-70A. The paper also looks toward how system identification could be used in new modeling challenges such as large tilt-rotors and uniquely configured unmanned aircraft.

Design and implementation of linear-quadratic-Gaussian stability augmentation autopilot for unmanned air vehicle

2009 ◽  
Vol 113 (1143) ◽  
pp. 275-290 ◽  
Author(s):  
C.-S. Lee ◽  
F.-B. Hsiao ◽  
S.-S. Jan
Keyword(s):  
System Identification ◽  
Linear Models ◽  
Additive White Gaussian Noise ◽  
Design Procedure ◽  
Flight Test ◽  
Control Synthesis ◽  
Linear Quadratic ◽  
Linear Quadratic Gaussian ◽  
Identification Methods ◽  
Stability Augmentation

Abstract The linear-quadratic-Gaussian (LQG) control synthesis has the advantage of dealing with the uncertain linear systems disturbed by additive white Gaussian noise while having incomplete system state information available for control-loop feedback. This paper hence explores the feasibility of designing and implementing a stability augmentation autopilot for fixed-wing unmanned air vehicles using the LQG approach. The autopilot is composed of two independently designed LQG controllers which control the longitudinal and lateral motions of the aircraft respectively. The corresponding linear models are obtained through a system identification routine which makes use of the combination of two well-established identification methods, namely the subspace method and prediction error method. The two identification methods complement each other well and this paper shows that the proposed system identification scheme is capable of attaining satisfactory state-space models. A complete autopilot design procedure is devised and it is shown that the design process is simple and effective. Resulting longitudinal and lateral controllers are successfully verified in computer simulations and actual flight tests. The flight test results are presented in the paper and they are found to be consistent with the simulation results.

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.

Numerical deconvolution using system identification methods

1988 ◽  
Vol 16 (1) ◽  
pp. 85-107 ◽  
Author(s):  
Sandor Vajda ◽  
Keith R. Godfrey ◽  
Peter Valko
Keyword(s):  
System Identification ◽  
Identification Methods ◽  
Numerical Deconvolution
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