Abstract
A 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.
Highlights
Aerodynamic models used for certification, performance evaluations, handling qualities evaluations, flight simulators, control law design, etc. must be validated and possibly updated with flight test data to match the real aircraft3-2-1-1 input signal doublet input signalFig. 1 3-2-1-1 and doublet multistep input signalsPSDnorm / ∆t, unitless Impulse DoubletFrequency, in Hz stability and control characteristics
Maneuver design for aircraft parameter estimation is usually done using an a-priori model of the aircraft, typically derived from wind tunnel experiments and CFD calculations
A new method to design single- or multi-input signals that can be used as maneuvers for aircraft parameter estimation was presented and discussed
Summary
Aerodynamic models used for certification, performance evaluations, handling qualities evaluations, flight simulators, control law design, etc. must be validated and possibly updated with flight test data to match the real aircraft3-2-1-1 input signal doublet input signalFig. 1 3-2-1-1 and doublet multistep input signalsPSDnorm / ∆t, unitless Impulse DoubletFrequency, in Hz stability and control characteristics. As described in [8], Marchand [14, 15] and Plaetschke et al [22] showed that evaluating the frequency response magnitude of the terms of each equation of the aircraft’s linear system is one possibility to identify the regions of identifiability of each derivative. These regions lie in the vicinity of the natural frequencies of the aircraft’s eigenmodes. A-priori aircraft models are subject to uncertainties, and the maneuver design must consider frequencies slightly above and below the expected eigenfrequencies
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