DTIC ADA520301: Neural-Network-Based Modeling of Rotorcraft Vibration for Real-Time Applications

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by  Defense Technical Information Center

(c)2000 American Instituf toAe eronautic& As stronauticr oPs ublished with Permissiof oAn uthor(s) and/or Author(s)' Sponsoring Organization.
Terh' *!* "?odeli"9 and Simulation
Technologies Conferd eEnnxcahe ibit AOO-37262
14-17 August 2000 DenverO,C
AIAA-2000-4305
NEURAL-NETWORK-BASED MODELING OF ROTORCRAFT
VIBR RRAEOATIFLO-TN IME APPLICATIONS
Sesi Kottapalli
Aeromechanics Branch
Army/NASA Rotorcraft Division
NASA Ames Research Center
Moffett Field, California
Summary
simulat nieoAxna. mple taken from currently used
The overall objective of this ongoing effort is to simule aVhttoe srrits ical Motion Simulator (VMSta)
providee ht capability ot modeld na simulate rotorcraft a hN siAgi hSS-MAfiVd Ae elmihtTeys, . pilotxeids,
aeromechanics behavin orerisa l-tune. This woeubld degree-of-freedom, real-time flight simulatort.I1" 3
accomplished by the addition of an aeromechanics allowe htg rsofr eatest motion rangy nfae lfoi ght
element to an existing high-fidelity, real-time helicopter simulator in the world. The Ames VMS can simulate a
flight simulatioa nfsAi.r st stepe hp,t eak vertical variety of aircraft including rotorcraft (not including
vibratio ehntp ta ilot floor locations awc onsideredni some aeromechanics behaviors), the Space Shuttle
this neural-network-based study ehfT.l ight conditions Orbiter, and others. A vibration model of the UH-60A
considered were level flights, rolls, pushovers, pull-ups, sabheen user omdf any yee ahAtr nosmit SMeVs
autorotatd iloannnasd ,ing f elNahrATesS .A/Army simulate pilot seat-shake.4 The associated flight test data
UH-60A Airloads Program flight test deahtta sbaawse based, seat-shaker algorithm does not involve neural
sw oduaae rprht cfaTroee. sent neural network training networks.
databases were created in a physically consistent
o mwomdeaTlninnge r a.pproaches, with different Currently, rotorcraft aeromechanics behaviors are not
physical assumptions, were considered. The first adequately modeled in simulators. These aeromechanics
approach involvea d" maneuver load factor" stahwat behaviors include pilot vibration and cabin noise.
derived using ehrt oll-ang ehptl denia tch-rateehT. Thus, future research ce odubirled cted towards inclusion
second approach involved ehtt hree pilot control stick of both cockpit vibration and noise. In this first-time
positions. The resulting, trained back-propagation study, helicopter vibratis omawno deled using neural
neural networks were small, implying rapid execution. networks. The non-inclusion of vibration in the
The present neural-network-based approach involving simulations of severe and/or complex maneuvers could
e phteak pilot vibratis uoawtni liza eq ndui asi-static have an adverse effect on pilot performance because such
manno estri mula netaex treme, time-varying pull-up maneuvers entail high vibration levels. It is thus
maneuver. For the above pull-up maneuver, the important to extend the existing real-time simulation
maneuver load factor approas cbawhe tt reroefr al-time capabilities by the addition of rotorcraft aeromechanics
simulation, i.e., produced greater fidelit syca,o mpared behaviors, especially vibration.
to the control stick positions approach. Thus, neural
networks show promise for use in high-fidelity, real- Existing neural-network-based work covered real-time
time modeling of rotorcraft vibration. rotor system flight test load monitoring systems for the
Navy SH-60 helicopter.5"7 On-line evaluatf ifoolin ght
Introduction vibratory s lsoatuae wpddhris eeTsde.5n t study,
however, covers neural-network-based, high-fidelity real-
In ordeo etr xpedite pilot trainins iig mti, portarnoft time ground based simulator model fiponigl ot floor
any flight simulator to achieve a high degree of vertical vibration for the Army UH-60A helicopter.
functional fidelity, i.e., the adequacy of piloted Neural network studien sor otorcraft performance,
acoustics dnd,a ynamics were initiateehdt ni
C eAohpmtyer© ri2yigc0hba0 tn0 Instfitoute Army/NASA Rotorcraft Division at NASA Ames
Aeronautics and Astronautics, Inc. No copyright is Research Center.8'13
asserted in the United States under Title 17, U.S. Code. The
U.S. Governm ar esoanyht alty-free licenso eet xercilslae The present neural-network-based results provide the
rights under the copyright claimed herein for Governmental capability to model rotorcraft aeromechanics behaviors
Purp loloAtsheesr . rie e grcrhehoasttpes yryvribegdh t in real-time. Specifically, the capabilities of the
owner. existing high-fidelity, real-time flight simulation would
1
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1. REPORT DATE 3. DATES COVERED
2000 2. REPORT TYPE 00-00-2000 to 00-00-2000
4. TITLE AND SUBTITLE 5a. CONTRACT NUMBER
Neural-Network-Based Modeling of Rotorcraft Vibration for Real-Time
5b. GRANT NUMBER
Applications
5c. PROGRAM ELEMENT NUMBER
6. AUTHOR(S) 5d. PROJECT NUMBER
5e. TASK NUMBER
5f. WORK UNIT NUMBER
7. PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES) 8. PERFORMING ORGANIZATION
US Army Aviation and Missile Command,Army/NASA Rotorcraft REPORT NUMBER
Division,Aeromechanics Branch,Moffett Field,CA,94035
9. SPONSORING/MONITORING AGENCY NAME(S) AND ADDRESS(ES) 10. SPONSOR/MONITOR’S ACRONYM(S)
11. SPONSOR/MONITOR’S REPORT
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13. SUPPLEMENTARY NOTES
14. ABSTRACT

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Prescribed by ANSI Std Z39-18
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e ebxtended. This woe uabcldc omephlti syhebd
addn aiaet irfooonm echanics element. 2. Study the fidelity considerations using a) neural
networks and the above maneuver load factor, and
For purposes of modeling the UH-60A vibration, two separate) lnbye ,ural ne tehhtrtwe deon rckaos ntrol
analytical approaches, involving different physical stick positions.
assumptions, were considered. This resuln ttehidr ee
neural-network-training databases. The first database . 3Quane athidftvy antad dgniesasa dvantagf ueoss ing
involved a maneuver load factor that was derived using the three training databases.
the helicopter roll-s aptin idtgcnlhae -re sahetTec.o nd
and third databases involved the three pilot control stick Vibration Neural Network Databases
positions. In the first and second databases, the
helicopter gross weigh saiwtn celhut fod eneo dsa e hTsourcee ht fo presently usedw ar dataeht saw
neural network inputs. The third database was the same NASA/Army UH-60A Airloads Program flight test
as the second database, except that the gross weight was database. ' The present study included the following
not included. flight conditions: level flights, rolls, pushovers, pull-
ups, autorotations, and landing flares. The creation of
In general, to obtain a time varying, step-by-step the present three compact neural-network-training
simulae tphitoi flono t vibration duringa m aneuvera, databases involved a substantial amount of manual
neural network based time-series mete hub onsaedc d. effort and time.
However, suche pmrhee ecstteohr nmoantd,p sIlex .
first-time modeling study using neural networks, a Descriptf iPoorne sent Databases
static-mapping approach involving the peak vibration
level was followed. This implied that each flight A single neural network output,l tclhoarme eomotn
conditios ancw haracterizes tpi dybe ak vibrationehT.
training databass ceawos,n sidere ephdTe. ak, N/rev
e presenht, peak fut-vtiilbizor iapntiogosns-ibbia lsiteyd pilot vertical floor vibration, peak PVV, was the neural
static mappinga niq uasi-static manner ot simulate time network outpute h.To verall approach usedo t obtain
varying maneu savalewsros investigatede.h tT ,hsait the peak PVV is discussed later, under "Database
fidelity of a quasi-static, real-time simulation was Construction Example: I Pull-up"d na also under
studieA qdu .asi-static approacht cowanipllt lulare "Database Construction Example II: Autorotation."
dynamic efy fmaemc id estnphssat r, edictiof nor elevant
maximums and their associated phases. Also, a time-
Databa 1sTe his neural network training database
series analysis using neural networks will capture the
involved six inputs that are given as follows:
maximums and phases more accurately, compared to a
quasi-static approact hIs.h oue lnbdo ted thehatt
) iAdvance ratio,
present quasi-static approach represents one way of
) ii Gross weight, Ibs.
simulating maneuvers.e hT present work provideseht
iii) Main rotor rotational speed, RPM.
capability of real-time simulation of pilot vibration for
iv) Density ratio.
e htentire UH-60A flight envelope.
v) Maneuver load factor, MLF, discussed below,
vi) Ascent/descent rate, fpm.
The present use of neural networks was justified because
of the following two reasons. First, trained neural
The MLF, a non-dimensional parameter, was used to
e nreabtpw indolarykc es xecun taeaddv ,antan rgeiea l-
characterize aircraft maneuvers involving simultaneous
time applications. Second, neural networkn pasc erform
non-zero roll-angle and pitch-rate. In the present study,
multi-dimensional, nonlinear curve fin tatidanvg a,ntage
s dawe fe FfihLtonth Myelelbd o wing equation:
in high-fidelity applications. The present work is
considered to be a generic methodology and is not
Maneuver load factor, MLF =
specific to the presently considered rotorcraft
[1 / cos (roll-angle)] *
configuration.
+ (p 1itc [h-ra* atei ]r s)pge e/d
Objectives
(1)
ephrTesent neural-network-based modeling study
where "g" is the acceleration due to gravity. The flight-
invoe plvheiantkg , N/rev pilot floor vertical vibration path axis system16 was used. The purpose of the MLF
hade ht following three objectives:
was to compactly represent complex maneuvers using a
single, physics-based parameter. eDhepte nndoing
1. Create two compact neural network training
reference axes system used, other parameters can be
databases, one involving the helicopter body
derived, and this would result in slightly different
motions and the other involving the pilot controls.
formulations.
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e phrTesent study attempo trtse conceihlte
Databas1 ien voo lavwierctd raft paramete eahrnstg ,le- aeromechad nfnliicags ht mechanics aspec nttshI. is
of-banke ht dna pitch-rate.e hT othero wt databasesod initial study, unfiltered time history records are shown.
not involve these two parameters, but involve the three rfolFight mechanics considerations, helicopter body
pilot control stick positions. 0 re1a eidm r/osapemotc ortptaionunt. s
Databas2 e This neural network training database Figu1 srhe e othiwmts ee ihhnistst tafonorty aneous
involved eight inputse g trihs vafaaoe tnl lows: pilot floor vertical acceleration for the above pull-up.
To obtain a time varying, step-by-step simulation of
i) Advance ratio. the acceleration time history shown a Fn in,ei 1gu. ral
ii) Gross weight, Ibs. network based time-series mete hbu oenshaedtc ndI.
iii) Main rotor rotational speed, RPM. present modeling study using neural networks, a static-
iv) Density ratio. mapping approach involving the peak vibration level
v) Collective stick position%, was followed. Figure 2 shows the pitch-rate time
) iLvateral stick posit%ion , historye ht rof same counter, 11022. Figure3 shows
vii) Longitudinal stick positio%n, the corresponding time-histories of the three pilot
viii) Ascent/descent rate, fpm. control stick positie ochonltsl ,ective, longituddninaa l
lateral cyclics. It should be noted that the filtered pilot
Database 3 This neural network training database was control stick position variations wot couonldn etahitn
obtained after removing the gross weight from the high frequency components present in the unfiltered data
database2 input list. Thus, database3 i nvolved seven. shown in Fig. 3.
inputs thae ragt ivens af ollows:
Pilot Floor Vertical Vibration Figure 4 shows the 4P
i) Advance ratio. component of the pilot floor vertical acceleration. This
ii) Main rotor rotational speed, RPM. 4P component, Fig. 4, was obtained by breaking up the
iii) Density ratio, time history record, Fig. 1, into intervals of 8
iv) Collective stick position,% revolutions eachn .I this studyP 4 ec,ht omponentfo
v) Lateral stick position, % e phtilot floor vertical accelerats iraoeewnhf te srar eodt
vi) Longitudinal stick position, % pilot vertical vibration,s PaVwV V. PVrePse enhtlyt ,
vii) Ascent/descent rate, fpm. obty apienbrefd orma ihnag rmonice ahnatl yfsois1 5
acceleration time-history in which the individual time-
The flight test data were represented in the neural segments (time windows) were eight rotor revolutions
network training databases in a physically consistent long. Some dyne aabmc ctiocun rea fytfeaelmcyts
manner. Physically consistent refers to the manual modeled if the sample length used in the harmonic
extraction of the correct values of relevant parameters, analysis is too large. At the same time, a too-small
e.ge ch.to, rrect pitch-rate associated wita hp ull-up sample length will introduce spurious dynamic
maneuver. Presently, the correct pitch-rate was taken as information. Future, detailed studies could focus on
that corresponding to the peak PVV. Let the peak PVV determining an appropriate sample length for the present
e choTrre c.oIta c t p i=ctmiua ttcre hs -dareawftien ed application. Such a time window study would need to
as that an lgsoIe neo eh.c rcaTtul ,r r= titanim g te considerl la maneuvers, make detailed comparisonsfo
peak-PVVs -datiiwmff Teer r, doeifnft ferent maneuvers, the resulting PVV's, and determine the appropriate
ande b ot dah individually determined. sample length. Figure4 shows thae thtp eraof VkVP
the above pull-ups aw 0.22 g's.
Database Construction Exampl: eIP ull-up
Pitch-Rate The correct pitch-rate, used in calculating
the maneuver load factor in database 1, for the above
A pull-up maneuvet ara pproximate0 l2ky1n ostaws
pull-up maneuver, counter 11022, was that
considered in this example. In the UH-60A flight test
database,5 1c4' 'ounter 11022 representea pd ull-up corresponding to the peak PVV, Fig. 4. The correct
pitch-rate, Fig. 2, was determined to be 9.6 deg/sec.
maneuver. This maneuver represenn teasx treme
The subject pull-up involved a negligible roll-angle,
maneuver. The pitch-rate associated with this particular
maneuver is extreme, one that was employed resula tm innaig neuver load= 2f .Fa0cL.tMor,
intentior tneoas elfttl hynp autor pm eorisgboehs t ,
empl epohity lyo oeabtdt c complisa hs udden evasive Pilot Control Stick Positions The three control stick
actr iotohFnis. flight conditie oghnrt, oss wesiagwht positions (databases 2 and 3) for the above pull-up
16055 Is e b2aha ewtrs5h dd to,M 5nvtaP,ao Rnr ce maneuver, counter 11022, were manually obtaisnaed
e s t0rahia.mw2Tti7eo .history record 7d3u rsaatiwon those corresponding to the peak PVV, Fig. 4. The
seconds (156 rotor revolutions). positioe hcnt foso llective, later danlalo ngitudinal
3
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(c)2000 American Institute of Aeronautics & Astronautics or Published with Permission of Author(s) and/or Author(s)' Sponsoring Organization.
csyacwlic Vce otihVnmtt rPeto a wles hhetn function network ro back-propagation network, etc.)dna
maximum were 76%, 6d 05n%7a%, , respectively. its complexity (i.e., the number of processing elements
(PEs) and the number of hidden layers). The present
Database Construction Exam :pAIIlue torotation overall neural network modeling approach8"13 consists of
first determinine ghbt est typf eon eural netwoerb kot
An autorotatiot aan pproximate0 lk6yn soawts used and then simplifying the network as much as is
practical.
considen rtheidi se UehxHat- m6n0pIAle .flight test
databao scweo,t1u e4nh'1tt5e rs 115 d31na91 540
represented two segments of the selected single Determininge ht best typef o neural network usually
autorotation conditionr o.F this flight conditione,ht involves selecting either a radial-basis function (RBF)
approximate gross weigs ha1wt 5910 Ibe shrt,o tor or a back-propagation network. The RBF network
Re ahPs d2ta Mvdw5na6 an, ce ras 0atiw.eo1 h7T. (Moody-Darken versioe bun nas)c edn im ost situations
time history record duratir eooafn ch se7g2m seawnt in which one would consider using a back-propagation
seconds (115 rotor revolutions)e h.Tm ain rotor shaft networke hp.t 1nrI7e sent studye hbt, ack-propagation
power and the pilot collective stick position were the type of network was used.
two helicopter performance parameters requiroetd
establish autorotation conditions. Simplifye nihnetgt work involves reduce nihnutgm ber
of PEs and in a few cases, the number of hidden layers.
Figure 5 showse ht time historiee sht fpo ilot floor The number of PEs required depends on the specific
vertical accelee rmhatt aidoninan rotor shaft powreofr applicatioe nhdT.e terminatie ohat pfno propriate
segment 1. The reduction in shaft power, starting just number of PEs is done by starting with a minimum
prior to 10 seconds, marks the beginning of the number of PEs. Additional PEs are added to improve
autorotation phase. Figure6 showse ht time historyrof neural network performany crbee S deMurrRco iernhgt
e htcollective stick positionr ofs egmen.t1 between the test data and the neural network predictions.
Typically, five PEs are added at each step in this
Figu7 rse hoe twhimts e hi espthoitlr oifeto sf loor process. Addinr og otwth re a tat esEiP me fine-tunes
vertical acceleration and the main rotor shaft power for the neural network.
segment 2. The resumption of power to the shaft
occurs after 10 seconds and marks the end of the Ie hcft orrelation plot, comparing measure ddnpa redicted
autorotation phase. Figure8 show ehtsti me historroyf values, shows only small deviations from the 45-deg
the collective stick position for segment 2. Figures 7 reference line, the neural network has produced an
and 8 show that the post-autorotation phase occurs in acceptable representation of the subject test data. If the
segment 2 and starts after 10 seconds. plot shows poine 4htts5 f-wod feefolgl linehet,
presencf eo" bad" test dats aia ssumedA d. etailed
Pilot Floor Vertical Vibration Figure9 showP4s eht examinae thisotu fnob ject test databass eti hen required
o tidentifye ht source(s)e ht foe rrors associated with
coe mphipltoo tn ffeolno tor vertical accelerratoiofn
these test data.
segments 1 and 2. The 4P component was obtained
from the acceleration time histories shown in Figs. 5
The notation used in this paper to characterize a neural
a .n7Fd igu9 rse hows e tphh Peacta4t okm ponefont
network is described as follows. A neural network
the pilot floor vertical acceleration for the complete
architecture such as "4-25-5-1" refers to a neural
autorotation condition occurred during its post-
network with four inputs, twenty five processing
autorotation phase, segmen 2t( for this particular
elements (PEs)e ht ni first hidden layer, fivehte ni sEP
maneuver). Figu9 rse hsoawws e tpWhheataPt k
second hide odnueotnp uldat.ynear ,
0.26 g's.
The application of neural networks to full-scale
e ht roFabove, complete autorotation condition,eht
helicopter flight test vibratios cnao dnwadtua cted using
neural network inputs needed in constructing the three
the neural networks package NeuralWo IIor/rPPkLs US
databases were those corresponde phinte, Wogat Pk
(version 5.2) by NeuralWare.17 The present neural
Fig. 9, and involved data from segment 2, counter
netwS eMors rrRdakow irm ensionled bnseaash st enod
11540.
sque aehrtr efroso r reosfa ch processing element (PEni)
e hotutput layer. Gene SerMarRrls laoeywhrt ,
Neural Network Approach
characterizea mydb onotonic decrease wite hhnt umber
of training iterations. Ay llnsaorag, e differencneis
o aTccurately captue rrheet quired functional
the magnitudes of the neural network variables were
dependencies, the neural network inputs must be
mitigated by appropriate scaling. In the present
carefully selectedd naa ccounl lat roif mportant physical
application, the cost function used in minimizing the
traits that are specific to the application. The important
attributa en fose ural netws ttoiy errpka e (radial-basis
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S MRerrord ah equally weighted individual A comparison of Figs. 10-12 brought up an interesting
contributions. data-quality considerationt Is. houle dbn oten dFi igs
10-1w 1ex,e ptfh eaarit mental data points clo os0te. 10
enhTumb feotrr aining data pos ioanwvtse r 200. s w'gere modeleo pdt roduce neural-network-based
Approximately 25% of this training database involved results that were close to the +0.05 g's error line. In
maneuver-related poine thmst n,i aneuver categories Fig. 12, the neural-network-based result for one of the
referred to earlier. Here, maneuver-related refers to a "0.10 g's" experimental point clearly fell outside of the
flight condition for which the maneuver load factor, +0s e.'0grr5 orn elAixnaem .inatf idooan taba3se
Me phLrt enFsIe *.n1 t stude shyit,n gle neural showed that this po siaanwst sociated witha r oll-
network outpe hutU sawt H-60A peak, pilot floor maneuver. This implies dna wethc ateits sa oriytn clude
vertical vibration (peak PW). Neural-network-based the gross weight as an input for maneuvers for the cases
modeling resule hptt rsoef e raag WkiPv enn it his presently considered wie thght iven approach.
paper in the form of correlation plots. The neural-
network-predicted peak PW is plotted versus the Quasi-Static Real-Time Simulation
corresponding flight test peak PW.
Selected results taken from Figs. 10e -s1rha2 ownin
Results Table 1 in numerical form to show typical neural
network predicr triooeanfls -time constant flight
Figure 10 shows the correlation obtained using the condition simulation.e hT test PW'sr of four flight
maneuver load factor, MLF, approach (data.b)a1se conditione hst dnna eural-network-based PW's, using
Fi0 gsuh1e roceh owtrsr elation plot froa Mm I-S6O all three databases, are shown in Table 1. The present
10-5-1 back-propagation neural netwoe rbhkTa. ck- neural-network-based model is good for high-speed level
propagation network was trained for 4 million iterations flight, descent, climba dcn,a onstant turn flight
with a final RMS error of 0.07. Figure 10 shows that condition, Table.1
e chotrresponding err- 0o/+.r0- bs5eaah gwnT'ds .
maneuver load factor approach, datab, ag1sae ve e phTresent neural-network-based approach involving
acceptable results. the peak vibration was utilized in a quasi-static manner
to simulate an extreme, time-varying maneuver. This
Figur1 e1s howe hsct orrelation obtained usineght maneuver has been considered earlier, the pull-up at 120
control stick approach (da. 1 tsF)ahbi2go1auws res e knots, counter 11022. The pull-up maneuver's
e cohrrtelation plot fra Mom ISO 8-10-5-1 back- experimental timee p hhilitos ttro forlioefos r vertical
propagation neural network. The back-propagation acceleration, the pitch-rate, the collective, the lateral and
network was trained for 550,000 iterations with a final longitudinal cyclics, and the PW (test PW) were
RMS erf r0oo.0r 7. Fi1 gsuh1roe wse thhat t shownn i Figs. 1-4.A quasi-static approach willton
corresponding error-band was +/- 0.05 g's. The pilot capture all dynamic effects, and may miss the prediction
control stick approach, database 2, gave acceptable of relevant maximd uthnmeais r associated phases.
results. Also, a time-series analysis using neural networks will
capte muhrtae ximd upnmhaas ses more accurately,
Figu2 sr1he oe wchots rrelation obtained usienhgt compa aq rouetad si-static approach.
control stick approach (database 3, gross weight not
included). Figure 12 shows the correlation plot from a e phIrtne sent quasi-static approa ecthihmt, e history
MISO 7-10-5-1 back-propagation neural enehtwTork . variations shown in Figs. 2 and 3 were represented by
back-propagation network was trained for 1.75 million their average values over an 8-revolutioh segment
iterations with a final RMS error of 0.07. Figure 12 length. Approximately 20 averaged values were thus
shows that the corresponding error-band was +/- 0.05 used. These averaged parameter values were usoetd
g'e shpT. ilot control stick approach, database3 with prepa enrhete ural network inpur dotfsa tabases 1-3.
the helicopter gross weight not included, gave The previously trained neural networks, Figs. 10-12,
acceptable results. were subsequently executed using the above discrete-
values-based input time histories. Thus, three quasi-
To summarize the above results, Figs 10-12, both the static real-time simulations were o.bWtPa eihnt erodf
maneuver load factor approach and the pilot control
stick approach were showe br oent asonable approaches Figures 13a and 13b show the present quasi-static,
for statically-mapped vibration. In this context, Figs. neural-network-based pe erxhetdrtie cmrtiooe nftis me-
10-12 showe esda htmhtae t lf emovoe dleling accuracy varying maneuver,e ht pull-up, using botheht
could be obtained from either approach. Also, the maneuver load factor approach and the control stick
present, trained back-propagation neural networks were positions approach, respectivelye .hT time segments
small, implying rapid execution. d t, nhWe aotFhfe soPtis,e4gr t . usn epidr eparing
the neural network inputs in Figs. 13a and 13b, were
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(c)2000 American Institute of Aeronautics & Astronautics or Published with Permission of Author(s) and/or Author(s)' Sponsoring Organization.
exactly aligned with each othen rti ime, i.a e zd.aeh, ro conditions. The flight conditions considered were as
offset. Fa igs3hu1orew s that neural-network-bdansead follows: level flights, rolls, pushovers, pull-ups,
the tests WpweaPekr e withif neo a0 cs.0h'g 2o ther. autorotations, and landing flares.
Figure 13a also shows that the neural-network-based and
e httest peakV VpP hases differedy ba pproximately1 r pouFrpof smoes odeling UH-60A vibraotiwont ,
second. The control stick positions approach, Fig. 13b, analytical approaches were considered. This resulted in
resulted in an overshoot of the PVV amplitude. The three neural network training databases. The first
overshoot ranged from approximately 0.04 g's (database database involved a maneuver load factor that was
2)o t 0.10s 'g (databas eehT p.)3 eakV VpP hase derived using the helicopter roll-angle and its pitch-rate.
predictions diffy earbpepd roximate3 slye cor bnoodftsh e hTsecondd nat hird databases involvede htt hree pilot
cases, databases 2 and 3. control stick d pse onefhicsariostt t ninodnIs .
databases ehh,t elicopter gross weighs aiwnt cludedsa
e same neuhral b sthow ex ac4tly 1 dna a41Fig ures enheut rofaonl e network ie nthhpiuTrdts .database
network resultsn i sa Figs.d na a31 13b, except thateht esehcot en ssdahwm adtaea stabase, excepet hthta t
Fig. 14 neural network results were offset by one half- gross weight was not included. The resulting, trained
segment. s dTaohwo nmitse aintain physical back-propagation neural networks were small, implying
consistencye th,i mti .neei. , dome nahietnu ral network rapid execution.
inpue trse smuulhtsint gp rvteicbes r bdaeetiioning th at
modeled, Figs. 14a and 14b. An appropriate value of The present neural-network-based approach involving
the above offset can be determined by detailed parametric the peak pilot vibration was utilized in a quasi-static
t doa onsheo h4wnesr et.h1 Fatisgu rae w dnsatudi es manno setir muln aeatxe treme, time-varying pull-up
neural-ns e eteWwthswte otrp erPekda-k bnasaed maneuver. For the above pull-up maneuver, the
within 0.02 g's of each other. Figure 14a also shows maneuver load factor approach was better for real-time
t ehnhaett ural-networke t-hebts adts npeWade aPk simulation, i.e., produced greater fidelis tcayo ,mpared
phases were approximately coincident. to the control stick positions approach. Thus, neural
networks show promise for use in high-fidelity, real-
For the pull-up maneuver under consideration, the time modeling of rotorcraft vibration for piloted
maneuver load factor approach, Fig. 14a, gave better simulations.
real-time simulation, i.e., resultedn i greater overall
fidelity with reasonably accurate peak prediction and Acknowledgements
only slight phasing differen sccaeoesm ,hpta oretd
control stick positions approach, Fig. 14b. The control The author wishes to thank Bill Hindson, Jay Shively,
stick positions approach again resulted in an overshoot Matt Whalley, and Bob Chen, all of NASA Ames
of eht peakV VP amplitude, Fig. 14b.e hT overshoot Research Center, for their feedback and constructive
ranged from approximatelys o (00d'..gat10t 04a )b2as e suggestions.
g's (database 3). The peak PVV phase predictions
differedy ba pproximately2 seconds rofb oth cases, References
databases 2 and 3. The present use of a one half-segment
offset was strictly empirical, and worked for the 1. Aiken, E.W., Lebacqz, J.V., Chen, R.T.N, and
presently considered maneuver A.t horougdhna Key, D.L., "Rotorcraft Handling-Qualities Design
systematic variate ihoot ffnof set involvinehgt Criteria Development," NASA Conference
optimization of an appropriate cost function is required Publication 249, 5N, IAVSoIAlu/mA ermy
to obtain the best value of this important offset. Rotorcraft Technology Conference, March 1987.
There nrea oticeable phase differences betweee thnet st . 2 Chen, R.T.N, Lebacqz, J.V., Aiken, E.dWna.,
PW maximum and the neural-network-based PW Tischler, M.B., "Helicopter Mathematical Models
me achxointm trruoomlf ss tick dcansaes ,b F3ig1s . and Cont wrDaoLel velopmer noHft andling
14b. This implies that in future studies, the time lag Qualities Research," NASA Conference Publication
betwee ephnti lot control stick position inputs 2495, Volume II, NASA/Army Rotorcraft
(collective, lateral and longitudinal cyclics) and the Technology Conference, March 1987.
resulting PVV must also be accounted for when
preparing the neural network inputs. 3. Sweeney, C., Sheppard, S, and Chetelat, M.,
"Development and Operation of a Real-Time
Conclusions Simulation at the NASA Ames Vertical Motion
Simulator," AIAA Flight Simulation Technologies
The present neural-network-based results showed that it Conference, AIAA-93-3575, Monterey, California,
was feasibo lote btain reasonably accurate, real-time August 1993.
modf erools torcraft vibration under various flight
American Instituf tAoe eronaud tAincass tronautics
(c)2000 American Institute of Aeronautics & Astronautics or Published with Permission of Author(s) and/or Author(s)' Sponsoring Organization.
4. Plonsky, J.G., Development of Equations to 14. Kufeld, R.M., Balough, D.L., Cross, J.L.,
Improve Deficiencies in the GenHel UH-60A Math Studebaker, K.F., Jennison, C.d DBna.o, usman,
Model, Sikorsky Aircraft Division, United W.G., "Flight Tese tUhitn fHog -60A Airloads
Technologies Corporation, NAS2-11058, 1989. Aircraft," American Helicopter Society 50th Annual
Forum, Washington, D.C., May 1994.
5. McCool, K.M., Flitter, L.A., aand Haas, D.J.,
"Development and Flight Test Evaluation of a . 51d BBnaojo n.rJdMkim, an, W.S., "TRENDSA,
Rotor System Load Monitoring Technology," Flight Test Relational Database," User's Gudidnae
American Helicopter Society 54th Annual Forum, Reference Manual, NASA TM 108806, June 1994.
Virginia Beach, Virgy i1an9Mia9, 8.
16. Chen, R.T.N. and Jeske, J.A., "Kinematic
. 6 Haas, D.J., Milan, o.JF, litte, .Lr", Predictiofon Proe pHheetr ltfiicoeos p ntCeir oordinated Turns,"
Helicopter Component Loads Using Neural NASA Technical Paper 1773, April 1981.
Networks," Journal oe hfAt merican Helicopter
Society, January 1995, pp.72-82. 17. NeuralWorks Manuals: a) Reference Guide b)
Neural Computing c) Using NeuralWorks,
. 7 McCool, K dH.nMaaa .s, D.J., "Predictifoon NeuralWare, Inc., Pittsburgh, Pennsylvania, 1995.
Helicopter Airspeed and Sideslip Angle in the Low
Speed Environment," McCool K.d MHna.a as,
D.J., American Helicopter Society 53 Annual
Forum, Virginia Beach, Virginia, April-May 1997.
8. Kottapalli, S. "Neural Network Research on
Validating Experimental Tilt-Rotor Performance,"
to be published in the July 2000 issue of the
Journal of the American Helicopter Society. ALAA
16th Applied Aerodynamics Conference, AIAA-98-
2418, Albuquerque, New Mexico, June 1998.
. 9 Kottapa, lS"liA ,pplicatif oNon eural Networokts
Aeromechanics Problems," 24th European
Rotorcraft Forum, Marseilles, France, September
1998.
. 01Kottapa ,.l"SlEi ,xploratory St nuNdoey ural
Control of Rotor Noise and Hub Loads," American
Helicopter Society Technical Specialists' Meeting
for Rotorcraft Acousticd nAsa erodynamics,
Williamsburg, Virginia, October 1997.
11. Kottap, a."SlIldi ,entifi dcCantoianoftn roo l
Rotorcraft Hub Loads Using Neural Networks,"
American Helicopter Society 53rd Annual Forum,
Virginia Beach, Virginia, April-May 1997.
12. Kottapalli, S., Abrego, A., and Jacklin, S.,
"Applicationf oN eural Networko stM oddenal
Predict Rotorcraft Hub Loads," American
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Connecticut, October 1995.
13. Kottapalli, S. and Kitaplioglu, C., "Neural
Network Representationf oE xperimental Tilt-Rotor
Noise," 6th AIAA/CEAS Aeroacoustics Conference,
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American Instituf Atoe eronaud tAincass tronautics
rd
(c)2000 American Institute of Aeronautics & Astronautics or Published with Permission of Author(s) and/or Author(s)' Sponsoring Organization.
Table. 1N eural-Network-Based Predictionsf o Pilot Floor Vertical Vibration. PVVs.'g
Flight Condition Test Maneuver Load Factor Control Stick Control Stick. No Weight
Databas1 e Database2 Database3
Level flight, 135 knots 0.10 0.09 0.10 0.11
Descent, 160 knots 0.25 0.24 0.25 0.26
Climb, 62 knots 0.12 0.12 0.12 0.12
Turn, 45 deg, 145 knots 0.13 0.13 0.13 0.14
8
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4
1 1 T T
Flight Test Data:
3.5
NASA/Army UH-60A Airloads Program
d
_o
"•C 3 _ Pull-up, 120 knots, counter 11022
2
V
2.5
u
a
2
1.5
1
I
0.5
£ 0 I
10 15 20 25 30 35 40
Time, seconds
Fig. 1. Time history of pilot floor vertical acceleration, pull-up.
30 I 1 I I
Flight Test Data:
25
NASA/Army UH-60A Airloads Program
20 Pull-up, 120 knots, counter 11022
rUU-UJJ, L£\J KlHHa, WUU111C1 \.l.\J£.£. ^^
15
V
"O
a 10
8)
i
W
+4
'£
^^fj ^^
-5
____I____I____I____I____I____I____I
10 15 20 25 30 35 40
Time, seconds
Fig. 2. Time-history of aircraft pitch-rate, pull-up.
American Instituf tAoe eronau dtAincasst ronautics