Parameter Estimation of Highly Unstable Aircraft Assuming Linear Errors

Ozger, Erol

doi:10.2514/6.2012-4511

Cited by 3 publications

(5 citation statements)

References 3 publications

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“…This investigation is the follow-up paper of the investigation where the new estimator is tested with only linear errors 13 . It is important to note that in this investigation the implemented nonlinear errors are estimated by a linear error model.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore a new estimation method 12,13 has been presented which combines equation and output error estimation. The combined equation/output error approach 12,13 gives flexib ility in the estimat ion process where by means of an adjustable weighting among the equation and output error contributions in the cost function any estimator behavior can be emulated fro m a pure equation error to a pure output error approach.…”

Section: Introductionmentioning

confidence: 99%

“…The combined equation/output error approach 12,13 gives flexib ility in the estimat ion process where by means of an adjustable weighting among the equation and output error contributions in the cost function any estimator behavior can be emulated fro m a pure equation error to a pure output error approach. Co mpared with the SOEM , it has the advantage that the integration of the equations of motion which highly interfere with the control laws are not man ipulated by an arbitrary stabilization scheme.…”

Section: Introductionmentioning

confidence: 99%

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Parameter Estimation of Highly Unstable Aircraft Assuming Nonlinear Errors

Ozger

2013

AIAA Atmospheric Flight Mechanics (AFM) Conference

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Flight test data of an unstable feedback controlled aircraft (Eurofighter) is artificially generated with added linear and nonlinear (de-)stabilizing error on the deri vati ves comprising also process and measurement noise that is analyzed with three estimators: equation error, output error, and wi th the new combi ned equati on/ output error methods estimati ng linear deri vati ves. The investigati on focuses on the estimation performance in the equation and output error domains when the estimators are applied on nonlinear errors wi th a linear error model. Main outcome is that when the error becomes nonlinear , the matching performance degrades and the s pread in the estimates of the various estimators increases considerabl y where underestimati on of the l ocal characteristics such as the stability characteristics occurs. Measurement and process noise influences particularl y the estimates of the secondary deri vati ves. If the error destabilizes the aircraft consi derabl y even small deviations in the estimates may produces very different maneuver responses in the output error domain which makes pure equation error estimation unrel iable in this respect. Moreover, the matching performance of the estimator is best in its own domain but compromising the other domain whereas the combi ned esti mator produces a g ood balance in matching performance for both equation and output error domains . Nomenclatureforce coefficient c l = rolling mo ment coefficient c m = pitching mo ment coefficient c n = yawing mo ment coefficient f = system state function F = process noise distribution matrix g = system observation function G = measurement noise distribution matrix I = inertia matrix J = cost function K = maximu m nu mber of time samp les l  = reference cord m = size of unknown parameter vector RMSE(x) = root mean square error o f x M  = mo ment vector N = maximu m nu mber of time samp les N = order of polynomial p = size of observation vector p,q,r = roll, p itch, and yaw rate S = reference area t = time t 0 = initial t ime u = control input vector v = measurement noise V = freestream velocity w = process noise w pi = diagonal elements of W W = normalizat ion matrix x = state vector x 0 = initial condit ion of state vector x 1,2,3 = input signals 1,2,3 y = simu lated observation vector z = measured observation vector  = angle of attack  = angle of sideslip  = flaperon deflection  = weighting between equation/output error  = normalizat ion factor  = unknown parameters to be estimated  = leading edge sweep   = angular rate vector  = aileron deflection  = rudder deflection § Professor in Aeronautics, Esplanade 10, 85049 Ingolstadt, Germany, and AIAA Senio r Member.Downloaded by MONASH UNIVERSITY on November 26, 2014 | http://arc.aiaa.org | q dyn = dynamic pressure s = half span Subscripts/Superscripts 0 = static offset ADM = aerodynamic model ctrl = control surface deflections Engine = engine related FT = flight test related G = gravity related i,j,n = i-th, j-th, n -th sample  = angle of attack derivative  ...

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Parameter Estimation of Highly Unstable Aircraft Assuming Nonlinear Errors

Ozger

2013

AIAA Atmospheric Flight Mechanics (AFM) Conference

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“…This makes full flight simulator development very costly, because the data cannot be gathered during scheduled flights (specific experiments are required) and the dedicated flight tests last multiple flight hours [3]. Despite the high cost in aeronautics, the system identification is widely used for airplanes [4,5], rotorcrafts [6,7], projectiles [8,9] or pilot modelling [10,11] as it delivers very accurate mathematical models of these objects.…”

Section: Introductionmentioning

confidence: 99%

Bulletin of the Polish Academy of Sciences: Technical Sciences

Lichota¹

2020

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In this paper, quanizted multisine inputs for a maneuver with simultaneous elevator, aileron and rudder deflections are presented. The inputs were designed for 9 quantization levels. A nonlinear aircraft model was exited with the designed inputs and its stability and control derivatives were identified. Time domain output error method with maximum likelihood principle and a linear aircraft model were used to perform parameter estimation. Visual match and relative standard deviations of the estimates were used to validate the results for each quantization level for clean signals and signals with measurement noise present in the data. The noise was included into both output and input signals. It was shown that it is possible to obtain accurate results when simultaneous flight controls deflections are quantized and noise is present in the data.

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“…This is also true for high manoeuvrable aircrafts that are often designed as unstable, or have relaxed static stability, in order to improve their combat performance e.g. X-29 [3], X-31A [4], Eurofighter [5], as well as unmanned aerial vehicles e.g. helicopter-based Yamaha R-50 [6], Ikarus ECO [7] or even more complex aircrafts that recently catch a great deal of interest, such as Quadcopters [8] and Cyclocopters [9].…”

Section: Introductionmentioning

confidence: 99%

Output Error Method for Tiltrotor Unstable in Hover

Lichota¹,

Szulczyk²

2017

Archive of Mechanical Engineering

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This article investigates unstable tiltrotor in hover system identification from flight test data. The aircraft dynamics was described by a linear model defined in Body-Fixed-Coordinate System. Output Error Method was selected in order to obtain stability and control derivatives in lateral motion. For estimating model parameters both time and frequency domain formulations were applied. To improve the system identification performed in the time domain, a stabilization matrix was included for evaluating the states. In the end, estimates obtained from various Output Error Method formulations were compared in terms of parameters accuracy and time histories. Evaluations were performed in MATLAB R2009b environment.

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Parameter Estimation of Highly Unstable Aircraft Assuming Linear Errors

Cited by 3 publications

References 3 publications

Parameter Estimation of Highly Unstable Aircraft Assuming Nonlinear Errors

Parameter Estimation of Highly Unstable Aircraft Assuming Nonlinear Errors

Bulletin of the Polish Academy of Sciences: Technical Sciences

Output Error Method for Tiltrotor Unstable in Hover

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