kalman filter and neural network‐based icing identification applied to a340 aircraft dynamics

Kalman filter and neural network-based icingidentification applied to A340 aircraft dynamics

Rahmi AykanTurkish Airlines, Istanbul, Turkey, and

Chingiz Hajiyev and Fikret CaliskanIstanbul Technical University, Istanbul, Turkey

AbstractPurpose – The purpose of this paper is to maintain safe flight and to improve existing deicing (in-flight removal of ice) and anti-icing (prevention of iceaccretion) systems under in-flight icing conditions.Design/methodology/approach – A recent academic research on aircraft icing phenomenon is presented. Several wind tunnel tests of anexperimental aircraft provided by NASA are used in the neural network training. Five ice-affected parameters are chosen in the light of theseexperiments and researches. An offline artificial neural network is used as an identification technique. The Kalman filter is used to increase the statemeasurement’s accuracy such that neural network training performance gets better. A linear A340 dynamic model is selected in cruise conditions. Thislinear model is simulated in time varying manner in terms of changing icing parameters in a system dynamic matrix. The obtained data are used inneural network training and testing.Findings – Airframe icing can grow in many ways and many points on aircraft. In this research, wing leading edge ice occurrence is only considered atthe same level in both left and right wings. During ice growth other faults or anomalies are ignored.Originality/value – Existing icing sensors can only provide an indication about possible ice presence. They cannot give information of the exact levelof ice. However, the efficiency of current control system of changed model decreases. The proposed technique offers a method to find out the modelchanges under icing conditions.

Keywords Aircraft, Ice cover, System monitoring, Stability (control theory), Neural nets

Paper type Research paper

Nomenclature

aij ¼ i-th row j-th column of A, A(i,j)A ¼ aircraft dynamic matrixB ¼ aircraft control matrix

�c ¼ wing mean aerodynamic chordCD ¼ drag coefficientCL ¼ lift coefficientCM ¼ pitching moment coefficientDu ¼ covariance matrix of control input errorDd ¼ covariance matrix of system disturbancee ¼ network errorg ¼ network weights’ gradientG ¼ transfer matrix of system disturbanceH ¼ measurement matrixH ¼ Hessian matrix of networkIyy ¼ inertial moment per aircraft pitch axisJ ¼ Jacobian matrix of networkM ¼ covariance matrix of extrapolation errorNW ¼ neural network weights’ vectorp ¼ roll rate

pe ¼ estimated roll rate by KFP ¼ covariance matrix of estimation errorq ¼ pitch rateqe ¼ estimated pitch rate by KF

�q ¼ dynamic pressurer ¼ yaw ratere ¼ estimated yaw rate by KFS ¼ reference wing areau ¼ aircraft control vectorU ¼ estimated state vectorU1 ¼ total aircraft speedx ¼ state vectorv ¼ forward velocityve ¼ estimated forward velocity by KFv ¼ measurement disturbance vectorw ¼ downward speedz ¼ measurementsa ¼ angle of attackae ¼ estimated angle of attack by KFb ¼ side-slip anglebe ¼ estimated side-slip angle by KFdE ¼ elevator deflectiondF ¼ flap deflectiondH ¼ stabilizer deflectiondR ¼ rudder deflectionu ¼ pitch angleue ¼ estimated pitch angle by KFc ¼ yaw anglece ¼ estimated yaw angle by KFw ¼ roll anglewe ¼ estimated roll angle by KFm ¼ training speed parameter

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Aircraft Engineering and Aerospace Technology: An International Journal

77/1 (2005) 23–33

q Emerald Group Publishing Limited [ISSN 0002-2667]

[DOI 10.1108/00022660510576019]

23

www.emeraldinsight.com/researchregister

www.emeraldinsight.com/0002-2667.htm

1. Introduction

The recent improvements and research on aviation havefocused on the subject of aircraft safe flight even in the severeweather conditions. As one type of such weather conditions,aircraft icing has been found considerably of negative effect onthe aircraft flight performance. Furthermore, thisphenomenon has resulted in several fatal accidents.The risks of the aircraft icing encountered during flight at

freezing temperatures and humid air have been known early1900s. Although it has been seen that icing could behazardous at every flight phase, take-off and landing havebeen affected most importantly. In addition, ice may occur onwings, control surfaces, horizontal and vertical stabilizers,fuselage nose, landing gear doors, engine intakes, fuselage airdata ports and sensors and drain system outputs. This studyexamines the only wing icing occurrences. Icing duringgrounding of aircraft is out of this study.NASA has performed several flight tests for in-flight icing of

the aircraft DHC-6 Twin Otter since 1986. Ratvasky andRanaudo (1993) obtained very useful data regarding theeffects of aircraft icing to aircraft stability and control in early1990. As soon as aircraft icing was announced as a prior issueon 1997, NASA established a team called Icing ResearchGroup. Bragg et al. (1998, 2002) from Illinois University haveinvestigated aircraft icing from several different viewpointsand proposed a smart icing system. Miller and Ribbens(1999) tried to detect tail icing by evaluating the decrease ofelevator effectiveness via failure detection filter. In anotherapplication (Millers and Ribbens, 1999), these researchersused a state estimator as a type of Luenberger observer. Thesestudies showed that icing detection via statistical error analysisof states was more effective than online parameter estimation.With NASA support, Ratvasky and van Zante (1999)examined experimentally and analytically the effects of tailicing. Bragg et al. (2000) proposed a method for flightenvelope protection by identifying icing characterization.Melody et al. (2000, 2001a, b) applied H-infinity algorithm toicing identification problem. They claimed that proposedmethod is better than least square estimation methods andextended Kalman filter (KF) methods. Schuchard et al.(2000) have worked on tail icing detection and classificationby estimating icing affected parameters and sensorinformation via neural networks. Johnson and Rokhsaz(2000) have proposed a method detecting icing via neuralnetworks and Kohonen self organizing maps (SOMs). Byobserving neural network connection weights’ changes, theyhave tried to find iced and clean aircraft model via SOMs. Inthat research, the effects of atmospheric turbulences andelevator input signal to icing identification were presented.With respect to identification of degradation in

aerodynamic parameters and characteristics of flightdynamics due to aircraft icing, dynamic icing detectionsystem (DIDS) was proposed by Myers et al. (2000). Bragget al. (1999, 2001a, b) used hinge moment sensors in order todetect icing on control surfaces. They improved a neuralnetwork model to estimate stability and control derivatives.In this research, icing identification based on neural

networks and KF is applied to A340 aircraft.

2. Aircraft icing

In-flight icing decreases the aerodynamic quality of aircraftsuch that aircraft weight increases, drag increases, lift

decreases, and hence the effectiveness of angle of attack andpitch angle change. The experimental studies have showedthat, in the result of wing icing, drag may increase to values of500 percent, and lift may decrease to values of 40 percent(Jackson and Bragg, 1999; Broeren et al., 2002; Whalen et al.,2002). The effect on moments may vary. Accordingly, theeffectiveness of control surfaces may decrease. All thesedirectly affect aircraft safe flight. As well as this subject isclearly important in the respect of aviation safety, by takinginto account extra fuel consumption due to icing, it isimportant for economical reasons.By including icing effects, the control and stability of more

correct aircraft model improves flight performance, passengercomfort and tight flight plans. It is clearly obvious thatmilitary aircraft have to fly at all places on air and weatherconditions as possible. On the other hand, intensive air traffichas forced the aircraft that could fly at all weather conditions.Civil aviation authorities and other organizations, such as

Federal Aviation Authority, (FAA) and Joint AviationAuthority (JAA), which provide aircraft certificates andquality assurances, have restricted the flight of the aircraftwhich are not installed anti-icing system and icing detectionsystem. In order to make sure that whether aircraft is safe onicing weather conditions, or not, the flight tests are mandatedby these organizations prior to first aircraft approval. Theseflight tests are too time-consuming and expensive. Instead ofthese tests, flight simulations of exactly modeled iced aircraftby using modern technology products would be better foraircraft manufacturers. At least, these simulations couldsupport to flight tests data.Some icing sensors in nose sections are used on some

modern aircraft to detect in-flight icing. However, thesesensors only show an indication or a possibility for icing whenit comes to some levels. They do not measure the icing effectssuch as its shape, thickness and location. It is impossible toevaluate the degradation of aircraft performance due to in-flight wing and tail icing. Hence, the existing sensors do notprovide enough information to pilot or autopilot.By an improved icing detection, monitoring and control

system, pilot/aircraft system can safely continue its routeregardless of weather icing conditions within acceptable safetymargins.On the subject of aircraft in-flight icing detection and

identification, there have been not enough academic researchfor the time being. Especially, some icing related accidents inTurkey and worldwide during the last 20-25 years, andadditional requirements of civil aviation authorities haveforced researchers to work on this subject deeper. Severalworks related to aircraft is continuing with the support ofNATO, NASA and some universities. The specialworking groups have been assigned by these researchcenters for icing.

3. A340 aircraft dynamic model

In-flight icing detection and identification is applied to anunstable multi-input multi-output model of an Airbus 340.The aircraft is stabilized by means of a linear quadraticoptimal controller. The control gain brings all eigenvaluesthat are outside the unit circle, inside the unit circle. Themodel of the aircraft is as follows:

xðkþ 1Þ ¼ AxðkÞ þ BuðkÞ þGwðkÞ ð1Þ

Kalman filter and neural network-based icing identification

Rahmi Aykan, Chingiz Hajiyev and Fikret Caliskan


Volume 77 · Number 1 · 2005 · 23–33

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where A is the system matrix; B the control distributionmatrix; u the control input vector; and w the system noisewith the following statistical characteristics:

E½wðkÞ� ¼ 0; E½wðkÞwTð jÞ� ¼ DdðkÞdðkjÞ

G is the transition matrix of system noise, and d(kj) is theKronecker symbol. The aircraft state variables are:

x ¼ ½v a q u b p r f C �T

where v is the forward velocity; a the angle of attack; q thepitch rate; u the pitch angle; b the side-slip angle; p the rollrate; r the yaw rate; f the roll angle; and c the yaw angle.The aircraft has five control surfaces and hence, the five

control inputs are:

u ¼ ½dH dE dF dA dR�

where dH, dE, dF, dA and dR are the deflections of stabilizer,elevator, flap, aileron and rudder, respectively.

4. KF for the A340 aircraft model estimation

In order to obtain efficient train samples by decreasing noiseeffects, KF is used in this study. Below the KF to estimate theA340 aircraft motion is designed.Let us define the parameters vector as:

UTðkÞ ¼ ½nðkÞ;aðkÞ; qðkÞ; uðkÞ;bðkÞ; pðkÞ; rðkÞ;fðkÞ;cðkÞ�

and apply the KF to estimate this vector.The measurement equations can be written as:

zðkÞ ¼ H UðkÞ þ vðkÞ; ð2Þ

where H is the measurement matrix, i.e. 9£ 9 unit matrix,and v(k) is the measurement disturbance, and its mean andcorrelation matrix, respectively, are:

E½vðkÞ� ¼ 0; E½vðkÞvTð jÞ� ¼ RðkÞdðkjÞ:

The KF equations are:

UðkÞ ¼ Uðk2 1Þ þ PðkÞHTðkÞR21ðkÞ{zðkÞ

2HðkÞUðk2 1Þ}ð3Þ

PðkÞ ¼ MðkÞ2MðkÞHTðkÞ½RðkÞ

þHðkÞMðkÞHTðkÞ�21HðkÞ MðkÞ

ð4Þ

MðkÞ ¼ APðk2 1ÞAT þ BDuðkÞBT þGDdðkÞGT ð5Þ

where P(k) is the covariance matrix of the estimation error;M(k) the covariance matrix of the extrapolation error; andDu(k) the covariance matrix of the control input error.Since the aircraft is unstable, it is stabilized by the linear

quadratic control technique. The performance index to beminimized is as follows:

J ¼Z t

0

½xTQxþ uTRu �dt ð6Þ

where Q is a semi-positive definite symmetric matrix and R isa positive definite symmetric matrix. The control input iscomputed as:

u ¼ R21BTKx ð7Þ

where the matrix K is computed from the following Riccatiequation:

ATK þKAþQTQ KBR 1BTK ¼ 0 ð8Þ

5. Parameters affected by the icing

As explained in Section 2, icing results in decreasing aircraftaerodynamic performance, which is affected by changes in lift,drag and pitch moment, and their effectiveness with regard toaircraft position angles and velocities. In commonrepresentatives of aircraft linearized dynamic equations, thiseffect may be reflected by stability and control derivatives.Especially, the researches in NASA Icing Research Group andIcing Institute of Illinois University (Melody et al., 2001b)have showed that the most affected parameters from in-flightwing icing are the followings:

CDa¼›CD

›a; CLa

¼›CL

›a; CLq

¼›CL

›q; CMa

¼›CM

›a; CMq

¼›CM

›q

where CDa; CLa

; CLq; CMa

; and CMqare stability derivatives;

CD, CL, CM are drag, lift and pitch moment coefficients,respectively. The change in lift coefficient with a change inangle of attack, CLa

; often called the lift curve slope. The liftcurve slope for the total airframe includes the componentsdue to the wing, fuselage, and tail. For most conventionalaircraft, it is generally true that the wing contributes 85-90percent to the value of CLa

(McLean, 1990). As well as iceaccumulation increases CDa

; it decreases CLa; CLq

; CMa; and

CMq(Bragg et al., 2000).

Stability and control derivatives are usually found fromwind-tunnel tests at first. Unfortunately, unavoidabledifferences between test environment and flight conditions,the wind-tunnel test data are considered only as initialestimates.In this study, as being in the previous research on icing, the

changes of other derivatives are assumed small and negligible.In the aircraft equations, sometimes these derivatives can bewritten as a dimensional format:

XwðCDaÞ ¼ 2�qSðCDa

2 CLÞmU1

ð9Þ

ZwðCLaÞ ¼ �qSðCLa

þ CDÞmU1

ð10Þ

ZqðCLqÞ ¼

�qSCLq�c

2mU1

ð11Þ

MwðCMqÞ ¼

�qSCMq �c

IyyU1

ð12Þ

MqðCMqÞ ¼

�qSðCMqÞð�cÞ2

2IyyU1

ð13Þ

where �q is dynamic pressure, S the reference area; U1 the totalaircraft speed, �c the wing chord, and Iyy is aircraft inertialmoment per aircraft pitch axis (Roskam, 1982). Like manystudies, this study uses the terms with angle of attack, a,instead of downward speed, w, in the aircraft state spaceequations in accordance with

a ¼ w

U1

: ð14Þ




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When aircraft linearized equations are examined, it is easilyfound that all these derivatives are in the matrix A shown inSection 3. Icing affected parameters of A340 aircraft dynamicmodel are A(1,2), A(2,2), A(2,3), A(3,2) and A (3,3), whichare written from former equations (9)-(13) as:

Að1; 2Þ ¼ k1ðCDa2 CLÞ ð15Þ

Að2; 2Þ ¼ k2ðCLaþ CDÞ ð16Þ

Að2; 3Þ ¼ k3CLqð17Þ

Að3; 2Þ ¼ k4CMað18Þ

Að3; 3Þ ¼ k5CMqð19Þ

where ki, i ¼ 1; 2; 3; 4; 5; consists of all other flight parameterswhich are considered constant for a certain time. Theseconstants may be calculated from certain flight conditionssuch as take-off, climb, cruise, and landing.In the simulations at Section 7, A(1,2), A(2,2), A(2,3),

A(3,2), and A(3,3) are expressed as a12, a22, a23, a32, and a33,respectively. In this study, in the duration of 2min, four ice-affected parameters are assumed to decrease their halves, andone parameter increase by 50 percent more.

6. Design of neural network model to estimateicing affected parameters

Neural networks have increasingly been shown as viable toolsfor mapping nonlinear systems and for the purpose ofparameter identification. It is a very efficient method in theanalysis of nonlinear and complex models if enough data areavailable for its training phase. Unfortunately, icing in flightoccurs in many different ways, and there is no enough trainingdata available regarding stability and control derivatives.There are little data only for a few research aircraft obtainedfrom tunnel test or flight test. After enough data are picked upfrom other methods, neural networks may be used effectivelyfor control. This study aims to find the stability and controlderivatives of clear and iced configuration. By monitoring theflight data, changes in these derivatives are found, and a faultsignal can be built up according to change level.The neural network has many interesting, complex, and

attractive features such as parallel processing, learning, self-organizing, nonlinear capabilities. Neural networks haveinherent parallel properties which provide a robust and fault-tolerant structure. Networks are practical for aircraftapplications because, following initial training, they processinformation very rapidly. Rapid computation can be achievedbecause the majority of mathematical operations involveaddition, subtraction, or multiplication (Campa et al.,2002b). A quick response in a certain time frame is especiallycritical for icing determination since ice accretion during flightat low altitude requires immediate action. Neural networks alsohave the capability to be trained online using real data or off-linewith recorded or simulated data (Campa et al., 2002a).In this study, since there are nine states measured and five

parameters to be estimated, a neural network structure havingnine inputs and five outputs is presented. Two hidden layersare proposed. These three layers have these activationfunctions, respectively: logarithmic, tangent and linear. Thisneural network is trained with the estimates of KF.For training method the Levenberg-Marquardt

Backpropagation algorithm is used to maintain second-order

training speed without having to compute the Hessian matrix,H. When the performance function has the form of a sum ofsquares (as is typical in training feedforward networks), thenthe Hessian matrix can be approximated as:

H ¼ JTJ ð20Þ

and the gradient, g, can be computed as:

g ¼ JTe ð21Þ

where J is the Jacobian matrix which contains first derivativesof the network errors with respect to the weights and biases,and e is a vector of network errors. The Jacobian matrix canbe computed through a standard backpropagation techniquethat is much less complex than computing the Hessian matrix.The Levenberg-Marquardt algorithm (LMA) uses thisapproximation to the Hessian matrix in the followingNewton-like update:

NWkþ1 ¼ NWk 2 ½ JTJþ mI �21JTe ð22Þ

where NWk is a vector of current weights and biases, and m isthe parameter of LMA to make the network faster and moreaccurate every step forward. If m is zero, the method becomesthe basic Newton’s optimization method. When m is large,this becomes gradient descent with a small step size. Newton’smethod is quicker and more accurate near an error minimum.Therefore, the aim in LMA is to shift toward Newton’smethod as quickly as possible.

7. Simulations

The proposed method is applied to several modelconfigurations. Both training and validation are performedeither on only clean or only iced A340 model. Batch size fortraining is chosen such that icing can be detected within acertain time frame. To compensate measurement noiselevels during the system identification stage, all states arefiltered through KF. Model noises are considered atacceptable levels.Figures 1-9 show the model states and KF estimates.

Dashed lines and solid lines represent actual states and KFestimates, respectively.

Figure 1 Aircraft actual speed (v), its KF estimate (ve-ve), and the errorbetween them in meter/second




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Figure 2 Aircraft actual angle of attack (a-alpha), its KF estimate(ae-alphae), and the error between them in radian

Figure 3 Aircraft actual pitch rate (q), its KF estimate (qe-qe), and theerror between them in radian/second

Figure 6 Aircraft actual roll rate ( p), its KF estimate ( pe-pe), and theerror between them in radian/second

Figure 4 Aircraft actual pitch angle (u - theta), its KF estimate(ue-thetae), and the error between them in radian

Figure 5 Aircraft actual sideslip angle (b - beta), and its KF estimate(be - betae), and the error between them in radian

Figure 7 Aircraft actual yaw rate (r), its KF estimate (re-re), and theerror between them in radian/second




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Figures 10 and 11 show control surface deflections.Figure 12 shows the performance of the proposed

neural network models at different stages: training,

validation and test. Figure 13 shows the approximate linearfit of the parameter a12 network outputs according to trainingdata.

Figure 9 Aircraft yaw angle (c – psi), its KF estimate (ce – psie), andthe error between them in radian

Figure 8 Aircraft actual roll angle (f - fi), its KF estimate (fe - fie), andthe error between them in radian

Figure 10 Control surface deflections, stabilizer and elevator, in radian

Figure 11 Control surface deflections, aileron and rudder, in radian

Figure 12 Training performance

Figure 13 Best linear fit of a12 (A: output, T: target)




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Figures 14-23 showneural networkoutputs anderrors at trainingstage. Dashed lines and solid lines on the upper side representneural network outputs and targets, respectively. At the lowerside, the errors between outputs and targets are shown.

Figures 24-28 show neural network outputs and errors atvalidation stage similar to those at training stage.Figures 29-35 show neural network outputs and errors at

test stage similar to those at training stage.

Figure 14 Training output history of parameter, a12

Figure 15 Training output error history of parameter, a12


Figure 17 Training error history of parameter, a22


Figure 19 Training output error of parameter, a23




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8. Conclusion and comments

In-flight icing affects several aircraft dynamic parameters. Inorder to evaluate icing effect on aircraft performance theseparameters are to be calculated. Five parameters primarily

affected by icing are taken in accordance with the previousstudy results. In this research, icing identification based onneural networks and KF is applied for the first time to A340aircraft model.The KF is used to increase state measurements’ accuracy

such that the training performance increases. An artificialneural network (ANN) is used as the identification technique.





Figure 24 Validation output history of parameter, a12

Figure 25 Validation output error history of parameter, a12




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The time interval for batch training is selected as the icingcould occur in the severe levels. The technique is developed toestimate the aircraft stability derivatives subject to change dueto icing. These are estimated and compared to those prior toicing. If the difference between them lies in a predetermined

tolerance band it is said that icing has not caused a fault.Otherwise there is an icing fault. In the simulations, thelongitudinal and lateral dynamics of an A340 aircraft dynamicmodel are considered, and the estimation of the stabilityderivatives affected by icing is examined. As example,


Figure 27 Validation output error history of parameter, a22


Figure 29 Test output history of parameter, a12

Figure 30 Test output error history of parameter, a12





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information downloaded from A340 Flight Data Recorder(Black Box) is applied to the trained neural network structure.A suitable neural network can estimate uncertain stabilityderivatives. This method is one of off-line estimations. Thismay be performed as online in future studies. The moremodel noise exists, the less validation noise becomes, buttraining performance gets worse.

Too many number of neurons in the network decrease thegeneralization of the network. Some validation samplesoutside the border of the training data values would havemuch more errors and result in rough estimation of ice-affected parameters.The obtained results give an insight about the different

types of icing detection that are possible via proposed method.

References

Bragg, M.B., Hutchison, T., Oltman, R., Pokhariyal, D. andMerritt, J. (2000), “Effect of ice accretion on aircraft flightdynamics”, Proc. 38th AIAA Aerospace Sciences Meeting andExhibit, Reno, Nevada, AIAA-2000-0360.

Bragg, M.B., Perkins, W.R., Basar, T., Sarter, N.B.,Voulgaris, P.G., Selig, M. and Melody, J. (2002), SmartIcing Systems for Aircraft Icing Safety, AIAA-2002-0813,Reno, Nevada.

Bragg, M.B., Perkins, W.R., Sarter, N.B., Basar, T.,Voulgaris, P.G., Gurbacki, H.M., Melody, J.W. andMcCray, S.A. (1998), “An interdisciplinary approach toin-flight aircraft icing safety”, Proc. 36th AIAA AerospaceSciences Meeting and Exhibit, Reno, Nevada, AIAA-98-0095.

Broeren, E.P., Eddy, H.E. and Bragg, M.B. (2002), “Effect ofintercycle ice accretions on airfoil performance”, AIAA-2002-0240.

Campa, G., Fravolini, M.L. and Napolitano, M.R. (2002a),“A library of adaptive neural networks for controlpurposes”, paper presented at the IEEE InternationalSymposium on Computer Aided Control System Design,Glasgow, Scotland.

Campa, G., Napolitano, M.R., Seanor, B., Fravolini, M.L.and Song, Y. (2002b), “Application of an improved LWRmethod to real-time aircraft parameter identificationproblems”, paper presented at the American ControlConference, Anchorage, AK.

Jackson,D.G. andBragg,M.B. (1999),AerodynamicPerformanceof an NLFAirfoil with Simulated Ice, AIAA 99-0373.

Johnson, M.D. and Rokhsaz, K. (2000), “Using artificialNeural networks and self organizing maps for detection ofairframe icing”, paper presented at the 2000 AtmosphericFlight Mechanics Conference, AIAA-2000-4099.

Figure 32 Test output error history of parameter, a22







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McLean, D. (1990), Automatic Flight Control Systems,Prentice-Hall, Cambridge.

Melody, J.W., Basar, T., Perkins, W.R. and Voulgaris, P.G.(2000), “H-infinity parameter identification for inflightdetection of aircraft icing”, Control Engineering Practice,Vol. 8, pp. 985-1001.

Melody, J.W., Pokhariyal, D., Merret, J., Basar, T. and Bragg,M.B. (2001a), “Sensor Integration for in-flight icingcharacterization using neural networks”, paper presentedat the 39th Aerospace Science Meeting and Exhibit, Reno,Nevada, AIAA-2001-0542.

Melody, J.W., Hillbrand, T., Basar, T. and Perkins, W.R.(2001b), “H-infinity parameter identification for in-flightdetection of aircraft icing: the time varying case”, IFACControl Engineering Practice, pp. 1327-35.

Miller, R.H. and Ribbens, W.B. (1999), The Effects of Icing onthe Longitudinal Dynamics of an Icing Research Aircraft,Number 99-0636 in 37th Aerospace Sciences. AIAA.

Myers, T.T., Klyde, D.H. and Magdaleno, R.E. (2000),“The dynamic icing detection system”, paper presented atthe 38th AIAA Aerospace Sciences Meeting and Exhibit,Reno, Nevada.

Ratvasky, T.P. and Ranaudo, R.J. (1993), Icing Effects onAircraft Stability and Control Determined from Flight Data –Preliminary Results, NASA TM-105977 (AIAA-93-0398,31st Aerospace Sciences Meeting and Exhibit).

Ratvasky, T.P. and van Zante, J.F. (1999), “In-flightaerodynamic measurements of an iced horizontaltailplane”, AIAA-99-0638, paper presented at the 37thAerospace Sciences Meeting and Exhibit.

Roskam, J. (1982), Airplane Flight Dynamics and AutomaticFlight Controls, Part I and II, Roskam Aviation andEngineering Corporation, KS.

Schuchard, E.A., Melody, J.W., Basar, T., Perkins, W.R. andVoulgaris, P. (2000), “Detection and classification ofaircraft icing using neural networks”, Proc. 38th AIAAAerospace Sciences Meeting and Exhibit no. AIAA-2000-0361, (Reno, Nevada), January 2000.

Whalen, E., Lee, S. and Ratvasky, T. (2002), “Characterizingthe effect of ice on aircraft performance and controlfrom flight data”, paper presented at the AerospaceScience Meeting and Exhibit, Reno, Nevada,AIAA-2002-816.

Further reading

Caliskan, F. andHajiyev,C. (2003), “Actuator failure detectionand reconfigurable control for F-16 aircraft model”, IFACAutomatic Systems for Building the Infrastructure in DevelopingCountries, Istanbul.

Gurbacki, M.H. and Bragg, M.B. (1999), Sensing AircraftIcing Effects by Flap Hinge Moment Measurement, AIAA-99-3149, Norfolk VA.

Gurbacki, H.M. and Bragg, M.B. (2001), “Sensing aircrafticing effects by unsteady flap hinge-moment measurement”,Journal of Aircraft, Vol. 39 No. 3, pp. 575-7.

Lyshevski, S.E. (1997), “State-space identification ofnonlinear flight dynamics”, Proceedings of the Conference onControl Applications, Hartford, CT, pp. 496-8.

Ribbens, W. and Miller, R.H. (1999), “Detection of icing andrelated loss of control effectiveness in regional andcorporate aircraft”, paper presented at the 37th AerospaceSciences, AIAA-99-0637.

About the authors

Rahmi Aykan was born in Sivas, Turkey in1970, finished Aircraft Engineering of IstanbulTechnical University in 1992, andMSc degree inthe same department in 1997. He has beenworking for Turkish Airlines, as an aircraftengineer in Technical Department, andcontinuing PhD degree in Aircraft Engineering

of Istanbul Technical University. He is preparing PhD thesis onthe subject of Aircraft Icing Identification by Neural Networksand Control Reconfiguration in Airframe Icing. His experienceis on maintenance, corrosion, structural repairs, automaticflight mechanics and control of aircraft. He is married, and hasone 4-year-old son. His hobbies are travelling to new places,seeing beautiful natural scene, watching sports, and reading.

Chingiz Hajiyev was born on 29 November1958 in the Kelbecer, Azerbaijan Republic. In1981 he graduated from Moscow AviationUniversity (Mascow, Russia) with honourdiploma on the field of “Automatic andInformation Systems” (MSc degree). Hereceived the PhD and DSc(Eng) degrees in

Process Control from Superior Certifying Commission at theCouncil of Ministers of the USSR from Azerbaijanian Scientificand Production Association (ASPA) “Neftgazavtomat”(Sumgait, Azerbaijan), in 1987 and 1993, respectively.From1987 to 1994 he worked as a scientific worker, senior scientificworker, chief of the Information-Measurement SystemsDepartment at the ASPA “Neftgazavtomat”. From 1994 to1996 he was a leading-scientific worker at the Institute ofCybernetics of the Academy of Sciences of Azerbaijan Republic.He was also a professor in the Department of Electronically-Calculated System Design, Azerbaijan Technical University,where he had been teaching 1995-1996. Since 1996 he has beenwith Department of Aeronautics and Astronautics, IstanbulTechnical University (Istanbul, Turkey), where he is currently aprofessor.Hehasmore than200 technical publications includingfive books. He is a full member of the International Academy ofNavigation and Motion Control (Russia, Saint Petersburg). Hewas awarded a grant from the International Science Foundation(USA) (1993). He is an adviser editor and on the executiveeditorial boards of a number of journals.His research interestinclude system identification, fault detection and isolation, faulttolerant aircraft control system design, aircraft parameterestimation and integrated navigation systems.

Fikret Caliskan received the BSc (1984) andMSc (1987) degrees from the technicaluniversity of Istanbul, Turkey, in electrical,electronics and control systems engineering,and the PhD (1993) degree from the universityof London, United Kingdom, in controlsystems engineering. He was a lecturer at the

technical university of Istanbul between 1993 and 1997, andhas been an assistant professor at the same university since1997. He was involved with various research and teachingactivities at Oakland University and Washington University,USA between 1999 and 2001. He is a referee for severalinternational journals. He published a book together withChingiz Hajiyev and has more than 30 publications. Hisresearch interest includes fault tolerant control systems,robust control, neural networks, and estimation.




Volume 77 · Number 1 · 2005 · 23–33

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kalman filter and neural network‐based icing identification applied to a340 aircraft dynamics

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