Cranfield University Stephen Carnduff System Identification of Unmanned Aerial Vehicles

239 Pages·2009·2.97 MB·English

Cranfield University
Stephen Carnduff
System Identification of Unmanned Aerial Vehicles
Dynamics, Simulation and Control Group
Department of Aerospace Sciences
School of Engineering
Submitted for the Degree of Ph.D.
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Cranfield University
Dynamics, Simulation and Control Group
Department of Aerospace Sciences
School of Engineering
Submitted for the Degree of Ph.D.
Academic Year: 2007-2008
Stephen Carnduff
System Identification of Unmanned Aerial Vehicles
Supervisor: Dr Alastair Cooke
14th August 2008
© Cranfield University2009. All rightsreserved. No part ofthispublicationmaybe
reproduced withoutthepermissionofthecopyrightholder
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Abstract
Theaimofthisresearch istoexamineaspectsofsystemidentificationforunmannedaerial ve-
hicles (UAVs). The process for aircraft in general can be broken down into a number of steps,
includingmanoeuvredesign,instrumentationrequirements,parameterestimation,modelstruc-
ture determination and data compatibilityanalysis. Each of these steps is reviewed and poten-
tial issuesthat could be encountered when analysing UAV dataare identified. Problems which
may be of concern include lack of space within the airframe to mount sensors and a greater
susceptibility to the effects of turbulence in comparison to manned aircraft. These issues are
investigated using measurements from two experimental sources. Firstly, Cranfield Univer-
sity’s dynamic wind tunnel facility is utilised, in which scale models are flown in semi-free
flight. Thecontrolsurfacesareactuatedsothatinputs,similartothoseusedwhenflighttesting
full-sizedaircraft,canbeappliedandtheresultantresponseofthemodelisrecorded. Measure-
mentsfroma1/12scalemodeloftheBAeHawkanda1/3scalemodeloftheFLAVIIRproject
demonstrator UAV are used. An added benefit of the facility to this work is that the wind tun-
nel models are comparable in size to the miniature class of UAVs. Therefore, practical issues,
similartothosefacedfortheseaircraft,areencounteredwiththewindtunnelexperiments. The
second sourceofexperimentaldataisUAVflight testdatasuppliedby BAE Systems.
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Acknowledgements
Firstly, I wish to thank my supervisor Dr Alastair Cooke, whose invaluable guidance and sup-
port has helped steer my work in the right direction over the last three years. I also wish to
acknowledgethe former Head of the Dynamics, Simulationand Control group MikeCook for
his encouragement and advice on all aspects of flight dynamics. Thanks must also go to other
members of the Dynamics, Simulation and Control group, including Dr James Whidborne, Dr
Ali Savvaris and Dr Sascha Erbsloeh. In particular, I am grateful to Sascha for lending me his
expertise when preparing for and carrying out the wind tunnel experiments. Thanks also to
Peter Thomas,who provideda valuablepairofhands forthetestsoftheDEMONmodel.
From BAE Systems, I am indebted to the project’s industrial mentor Chris Fielding for his
indispensable advice and support. I must also thank Chris for his help in arranging for me to
have access to a valuable source of flight test data and putting me in contact with the system
identification experts within the company. In this respect, I am grateful to Andy Perkins and
Chris Forrest for providingan aircraft manufacturer’s perspectiveon the subject and construc-
tivefeedback abouttheresearch I wasconducting.
Whiletryingtostrikethework-lifebalanceduringmytimeatCranfield,Ihavehadthepleasure
of meeting a wonderful group of people. For all the games of football, tennis, golf, barbeques
andtripstotheLakeDistrict,IwishtothankJasonBennett,RossChaplin,IanCowling,Simon
Croucher, Marco Hahn, Marco Kalweit, Sunil Mistry, John Murphy, Sanjay Patel, Michael
Porton, Adam Ruggles, YosukeShimada, Peter Thomasand Ben Thornber. A special mention
goestoBen,Sanj,andthetwoMarcos,fromwhomIliketothinkIhavealearnedalittleabout
computationalfluid dynamicsduringourmanycoffeebreaks together.
Finally, I wish to express my gratitude to my family and friends for all their love and support
over the last three years. I am especially thankful to my parents David and Evelyn, without
whomIwouldneverhavereached thispoint.
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Contents
Abstract v
Acknowledgements vii
Notation xviii
1 Introduction 1
1.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 SurveyofAircraft SystemIdentification . . . . . . . . . . . . . . . . . . . . . 3
1.3 ObjectivesoftheResearch . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
1.4 Sources ofExperimentalData . . . . . . . . . . . . . . . . . . . . . . . . . . 9
1.5 LayoutofThesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
1.6 SoftwareTools . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
2 Manoeuvre Design 13
2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
2.2 Frequency Sweeps . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
2.3 DesignbyStatisticalAnalysis . . . . . . . . . . . . . . . . . . . . . . . . . . 15
2.4 MultistepInputs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
2.5 InputDesignforDynamicWindTunnelModels . . . . . . . . . . . . . . . . . 23
3 Sensors andInstrumentation 29
3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
3.2 InertialSensors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
3.3 AttitudeDetermination . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
3.4 AirDataMeasurements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35
3.5 Analogue-to-DigitalConversionRequirements . . . . . . . . . . . . . . . . . 36
3.6 InstrumentationforDynamicWindTunnelFacility . . . . . . . . . . . . . . . 38
3.7 DeterminationofControlSurface Positions . . . . . . . . . . . . . . . . . . . 39
4 Equation Error Method 44
4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44
4.2 OrdinaryLeast Squares RegressionAlgorithm . . . . . . . . . . . . . . . . . . 44
4.3 SmoothingNoisyData . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47
4.4 NumericalDifferentiation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51
4.5 Accuracy ofParameterEstimatesand Identified Model . . . . . . . . . . . . . 55
4.6 ColouredResiduals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58
4.7 UAVYawingMomentExample . . . . . . . . . . . . . . . . . . . . . . . . . 60
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4.8 UAVNonlinearYawingMomentExample . . . . . . . . . . . . . . . . . . . . 64
4.9 DEMONDutchRoll Example . . . . . . . . . . . . . . . . . . . . . . . . . . 70
4.10 HawkShort Period Example . . . . . . . . . . . . . . . . . . . . . . . . . . . 75
5 Output Error Method 80
5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80
5.2 MaximumLikelihoodEstimation . . . . . . . . . . . . . . . . . . . . . . . . . 80
5.3 MinimisationofCost Function . . . . . . . . . . . . . . . . . . . . . . . . . . 82
5.4 CalculationoftheEstimatedOutput . . . . . . . . . . . . . . . . . . . . . . . 84
5.5 CalculationoftheOutputSensitivities . . . . . . . . . . . . . . . . . . . . . . 85
5.6 AlgorithmicVariations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86
5.7 ConvergenceCriteria . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88
5.8 Accuracy ofParameterEstimatesand Identified Model . . . . . . . . . . . . . 89
5.9 UAVShort Period Example . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91
5.10 DEMONDutchRoll Example . . . . . . . . . . . . . . . . . . . . . . . . . . 95
6 Frequency DomainEstimation 100
6.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100
6.2 TransformationofData . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100
6.3 LeastSquares Regression . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101
6.4 MaximumLikelihoodEstimation . . . . . . . . . . . . . . . . . . . . . . . . . 102
6.5 Frequency ResponseMethod . . . . . . . . . . . . . . . . . . . . . . . . . . . 103
6.6 UAVPitchingMomentExample . . . . . . . . . . . . . . . . . . . . . . . . . 105
6.7 DEMONShort Period Example . . . . . . . . . . . . . . . . . . . . . . . . . 106
7 EstimationIssues forUAVs 113
7.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 113
7.2 Estimationin thePresence ofAtmosphericTurbulence . . . . . . . . . . . . . 113
7.2.1 MaximumLikelihoodFilterError Method . . . . . . . . . . . . . . . . 114
7.2.2 AlgorithmforLinearSystems . . . . . . . . . . . . . . . . . . . . . . 116
7.2.3 AlgorithmforNonlinearSystems . . . . . . . . . . . . . . . . . . . . 120
7.2.4 Extended KalmanFilterMethod . . . . . . . . . . . . . . . . . . . . . 122
7.2.5 DEMON RollModeExample . . . . . . . . . . . . . . . . . . . . . . 124
7.3 ParameterEstimationfromClosed-LoopData . . . . . . . . . . . . . . . . . . 133
7.3.1 DataCollinearity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 133
7.3.2 Biased Estimation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 136
7.3.3 UAV RollingMomentExample . . . . . . . . . . . . . . . . . . . . . 139
7.3.4 UAV PitchingMomentExample . . . . . . . . . . . . . . . . . . . . . 144
8 Model Structure Determination 148
8.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 148
8.2 RegressionAnalysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 148
8.3 StepwiseRegression . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 151
8.4 UAVRollingMomentExample . . . . . . . . . . . . . . . . . . . . . . . . . . 154
8.5 HawkShort Period Example . . . . . . . . . . . . . . . . . . . . . . . . . . . 158
8.6 DEMONDutchRoll Example . . . . . . . . . . . . . . . . . . . . . . . . . . 160
8.7 MaximumLikelihoodModelStructureDetermination . . . . . . . . . . . . . . 162
8.8 UAVRollingMomentExample . . . . . . . . . . . . . . . . . . . . . . . . . . 165
8.9 HawkShort Period Example . . . . . . . . . . . . . . . . . . . . . . . . . . . 166
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