Model and Sensor Based Nonlinear Adaptive Flight Control with Online System Identification

259 Pages·2014·11.49 MB·English

Invitation
FF MM
ll MMooddeell aanndd SSeennssoorr BBaasseedd
ISBN: 978-94-6186-350-8 ii oo
gg
dd
turbulence hh
ee
t t ll NNoonnlliinneeaarr AAddaappttiivvee FFlliigghhtt CCoonnttrrooll On thursday
CC
aa
October 30, 2014
oo nn
wwiitthh OOnnlliinnee SSyysstteemm IIddeennttiiffiiccaattiioonn
nn dd at 12.30
tt
rr SS
oo
VTP ll ee ON Li Guo SUN
nn R
ww E
ss L
I
itit oo A will defend his thesis
hh rr
ENGINE_1 O O B B titled:
aa
nn
LEFT HTP ss
ll Model and Sensor
ii ee
nn
dd
R ee Based
R VATO S S N N Nonlinear Adaptive
ED LE yy oo
D E
U ss nn Flight Control
R tt ll
ee inin with Online
mm
ee
aa System
II rr
dd
Identification
E L E V A T O R enen AdAd
tt aa
ii
ff pp
ii The defense will
cc tt
ii
aa vv take place in the
tt ee
RIGHT HTP ii Senaatszaal at the Aula
oo
nn of TU Delft
ENGINE_2 Mekelweg 5, Delft
At 12.00,
prior to the defense,
N there will be a short
O
R
E
A I L RIGHT WING L Li Guo Sun presentation about the
i
thesis work
G
u
o

S
u
n
MODEL AND SENSOR BASED NONLINEAR
ADAPTIVE FLIGHT CONTROL WITH ONLINE
SYSTEM IDENTIFICATION
LiGuo SUN
(cid:3632)(cid:1220)(cid:3422)(cid:13557)(cid:17886)(cid:16892)(cid:8279)(cid:3521)(cid:2754)(cid:1366)(cid:5973)(cid:3230)(cid:11450)(cid:19860)(cid:13557)(cid:5725)(cid:3)
(cid:14368)(cid:17976)(cid:5322)(cid:20244)(cid:16002)(cid:6621)(cid:2156)(cid:3)
(cid:3)
(cid:4495)(cid:12545)(cid:3379)(cid:3)
MODEL AND SENSOR BASED NONLINEAR
ADAPTIVE FLIGHT CONTROL WITH ONLINE
SYSTEM IDENTIFICATION
Proefschrift
terverkrijgingvandegraadvandoctor
aandeTechnischeUniversiteitDelft,
opgezagvandeRectorMagnificusprof.ir.K.C.A.M.Luyben,
voorzittervanhetCollegevoorPromoties,
inhetopenbaarteverdedigenopdonderdag30oktober2014om12.30uur
door
LiGuo SUN
MasterofScience,NanjingUniversityofAeronauticsandAstronautics
geborenteTangshan,HebeiProvince,China
Ditproefschriftisgoedgekeurddoordepromotor:
Prof.dr.ir.M.Mulder
Copromotor:Dr.Q.P.Chu
Samenstellingpromotiecommissie:
RectorMagnificus, voorzitter
Prof.dr.ir.M.Mulder, TechnischeUniversiteitDelft,promotor
Dr.Q.P.Chu, TechnischeUniversiteitDelft,copromotor
Prof.dr.ir.M. Verhaegen, TechnischeUniversiteitDelft
Prof.dr.-lngF.Holzapfel, TechnischeUniversitätMünchen
Dr.ir.G.H.N.Looye, DeutschesZentrumfürLuftandRaumfahrt(DLR)
Prof.dr.A. Zolghadri, UniversityofBordeaux
Dr.ir.C.C.deVisser, TechnischeUniversiteitDelft
Prof.dr.ir.J.A.Mulder, TechnischeUniversiteitDelft,reservelid
Keywords: Fly-by-wire, Aerodynamic model, Adaptive control, Fault tolerant,
Reconfiguration,Flightenvelopeprotection,Simplexsplinetheory
Printedby: IpskampDrukkers,Enschede,TheNetherlands.
Coverphotocopyright©BenUllings,AviationPhotosInternational.
CoverdesignbyYazdiIbrahimJenie.
ISBN978-94-6186-350-8
PublishedanddistributedbyL.G. Sun.
Email: [e-mail]
Anelectronicversionofthisdissertationisavailableat
http://repository.tudelft.nl/.
Copyright©2014byL.G. Sun. Allrightsreserved. Nopartofthematerialprotected
by this copyright notice may be reproduced or utilized in any form or by any means,
electronic or mechanical, including photocopying, recording or by any information
storageandretrievalsystem,withoutpriorpermissionoftheauthor.
Tomyparentsandmywife

S
UMMARY
Model and Sensor Based Nonlinear Adaptive Flight
Control with Online System Identification
Li Guo Sun
Consensusexiststhatmanyloss-of-control(LOC)inflightaccidentscausedbysevere
aircraft damage or system failure could be prevented if flight performance could be
recoveredusingthevalidandremainingcontrolauthorities. However,thesafemaneu-
verability of a post-failure aircraft will inevitably be reduced due to the malfunction.
Non-conventional control strategies which rely on modern control techniques and
computationalpowerareessentialtocontrolsystemsinpost-failureflightconditionsto
extractthemostfromthereduced,remainingaircraftcontrolauthoritiesandrestorethe
flightperformanceofanaircraftorachieveasafelanding. Onesuchnon-conventional
controlstrategyiscalledactivefaulttolerantflightcontrol(FTFC),whichisdesignedto
detectchangesinanaircraft’sdynamicscausedbystructural,actuator,orsensorfailure
andaccommodatethedamageorfailureusinganadaptivereconfigurationmechanism.
TheactiveFTFCtechniqueisabletodealwithunanticipatedandmultiplesimultaneous
failures.
The overall architecture of an active FTFC system ideally should consist of a fault
detection and diagnosis (FDD) module, a state reconstruction unit, a reconfigurable
control component, a control allocation unit and a flight envelope protection (FEP)
unit. Generally speaking, FTFC systems can be classified into two types: model-
based FTFC systems and model-free FTFC systems, according to whether any of the
system’s components require an aerodynamic model at their core or not. A model-
basedFTFCsystemcontainsanaerodynamicmodelidentification(AMI)module,which
suppliesanaccurateaircraftmodeltoanindirectadaptivenonlinearcontrollerinthe
reconfigurablecontrolblock,toadynamicflightenvelopedeterminationalgorithmin
anFEPunit,ortoanFDDunit. Anaerodynamicmodelidentificationapproachusing
a physical, interpretable modeling structure can detect and even quantify structural
failuresoccurringintheaircraftstructureoroneofthecontrolsurfacesbymonitoring
changesinstabilityderivativesandcontrolderivatives.
There are many candidate control approaches which can achieve reconfiguration
whendesigningareconfigurableflightcontroller.Thesereconfigurablecontrolmethods
mayrelyonmanydifferentreconfigurationmechanismsrangingfromswitching,model
following,matchingtoadaptivecompensation.Thesemethodsincludenonlinearadap-
tive control which achieves reconfiguration through compensation, and this method
is receiving increasing attention in the flight control aerospace research community.
Nonlinearadaptivecontrolisdividedintodirectadaptivecontrolandindirectadaptive
vii
viii
control, the difference is that the latter requires an online system model. Indirect
adaptive control is also called model-based or modular adaptive control, which has
someadvantagesoverthedirectadaptivecontrolandothermodel-freecontrolmethods.
One advantage is that a modular control approach has the potential to yield a more
efficient controller which requires less control effort. Such an efficient controller can
beachievedbymaintainingusefuldampingtermsofanidentifiedsystemmodelinthe
closed-loop system. This is attributed to the good properties of many control design
techniques such as backstepping such that the dynamics of an original system can
be chosen to be canceled or maintained during a controller design process. Modular
adaptive control also has an inherited shortcoming, it can only guarantee input-to-
state stability, i.e. modular adaptive control cannot guarantee the stability of the
overallclosed-loopsystembecauseitsstabilityproofreliesonthecertaintyequivalence
principle.Theweaknessofthecertaintyequivalenceprinciple,i.e.convergenceproblem
ofthemodelparameters,canbeimprovedbyenhancingmodelaccuracyorreliability,
to do this, it becomes critical to develop advanced, powerful aerodynamic model
identificationapproachescapableofcapturingchangesinflightdynamicseitherduring
ahighmaneuveringflightmissionorapost-failurecondition.
Flight envelope protection is a necessary technique that should be applied by
controllerdesignerstopreventLOCincidents,takingintoaccounthighlymaneuvering
flighttasksand/orhighlyperturbedflightconditionsduetotheongoingfailure.AnFEP
componentshouldprovideapilotwithasafeflightenvelopeandposeconstraintsonthe
referencecommandsfedtoaninternalcontrollertomakethecommandsachievable.
Anaerodynamicmodelthatisvalidoveranentireflightenvelopeplaysacrucialrole
in full-envelope modular adaptive control and flight envelope protection. A globally
validmodelisrequiredformodularadaptivecontroltoenablethedesignedcontroller
to work properly in a large operating range. Once estimated, the global model in a
model-based adaptive control method can be stored for later re-use when the same
flight condition is revisited. Except being needed by a model-based controller, an
accurateaerodynamicmodelisalsorequiredforflightenvelopeprotection. Naturally,
theestimatedaerodynamicmodelhastobevalidforthecurrentaircraftconfiguration
overtheentireflightenvelopetoenableanevolutionalgorithmtoestimatetheboundary
of the safe flight envelope for the current flight condition. However, only a limited
number of model identification approaches are suited for estimating a globally valid
aerodynamic model, and each existing possible candidate has variant shortcomings
or limitations which make it hard to apply directly to identify an aircraft model. For
example,neuralnetworksusuallyyieldanontransparentmodelstructurewhichishard
to interpret using physical knowledge of the system, and they commonly encounter
a convergence problem. Most kernel methods fall into the nonparametric type of
methods,whichbynatureneedasmanykernelsasthedatapointsunderevaluation. It
shouldbekeptinmindthatonlyequation-errortypemodelidentificationmethodswere
investigated in the work reported here. The assumption was made that a sufficiently
accurateestimationofaircraftstateswasavailable.
Analternatemethodtothemodularadaptivereconfigurablecontrolapproachisthe
accelerationmeasurements-basedincrementalnonlinearcontrol(AMINC)method.An
accurateestimationofanaircraftishardtoachieveduringahighmaneuveringmoment
ix
oratatransientperiodwhentheflightperformanceishighlyperturbedduetoaircraft
failure. Incrementalnonlinearcontrollerssuchasincrementalnonlineardynamicin-
version(INDI),incrementalbackstepping(IBKS)andsensor-basedbackstepping(SBB)
aresuitedforreconfigurableflightcontroldesignsinthesensethattheydonotrequire
completeaircraftmodelknowledge.
The main research question for the research presented here was: How can an
advanced fault-tolerant flight control system be designed to increase the survivability
ofanaircraft?Thisledtotwosubsidiaryquestions:
• Howcanthecandidatefunctionapproximationmethods,i.e.multivariatesimplex
B-splinesandkernelmethods,beimprovedintermsofapproximationaccuracy
andcomputationalefficiency,tomeettheneedofmodel-basedadaptivecontrol
andonlineflightenvelopeprotection?
• Whatarethebenefitsofusinganaccelerationmeasurements-basedcontrolap-
proach, i.e. the sensor based backstepping, as an alternative to a model-based
adaptive control approach, when designing a reconfigurable flight controller to
dealwithaircraftfailuresinagenericfault-tolerantflightcontrol(FTFC)system?
Withregardtoreconfigurablecontrol,theidentifiedmodelshouldenablethecontroller
to achieve active reconfiguration and restore the control performance. To answer
thesequestions,fourdifferentglobalmodelidentificationmethodsandtwononlinear
incrementaladaptivecontrollersweredeveloped.
Twomodelidentificationmethodsuseaparametricmodelstructurenamelystan-
dard multivariate simplex B-splines. The focus was placed on how to achieve fast
parameterestimationduringtheresearchprocessforthesetwomethods. Inthethird
identificationmethod,anewmodelstructurecalledtensor-productsimplexB-splines
was extended from a single dimension case to a multidimensional case, with a focus
on demonstrating the advantage of this new compound model structure in terms of
theflexibilityinmodelstructureselection,computationalefficiencyandapproximation
power. Thefourthmethodusesakerneltypemodelstructurewhichisalsoparametric.
The new recursive kernel approach was developed by combining a classical recursive
kernelmethodwithanovelsupportvectorregressionapproach.
A model identification method using standard multivariate simplex B-splines has
manyadvantages,itcanavoidtheover-fittingproblemwhichoccurswithanordinary
polynomial method using a triangulation technique. The approximation power of a
simplexB-splinebasedmethodisdeterminedbytheper-simplexpolynomialorderand
smoothnessorder,andcanbeincreasedbyincreasingthedensityofthesubdomainsina
triangulation. ThissimplexB-splinebasedfunctionapproximationmethodguarantees
thatitsoutputisboundedbythemaximumandminimumB-coefficients,thisfacilitates
its certification for future real life applications. The linear regression formulation
of the simplex B-spline based method allows for applying most of the constrained
recursive parameter estimation methods. Furthermore, the simplex B-spline based
method has a sparse property, which can lead to high computational efficiency by
adopting distributed computation or other modern computing techniques. However,
asimplexB-splinemethodcaneasilyyieldalargeamountofunknownparametersifthe
functiondimensionexceeds4,whichresultsinahighcomputationalloadconsidering
thesmoothnessmaintainingandcovariancematrixupdating.