Progress in Astronautics and Aeronautics, Volume 241 - Advances in Intelligent and Autonomous Aerospace Systems

539 Pages·2012·16.37 MB·English

by  Valasek

Advances in Intelligent
and Autonomous
Aerospace Systems
EDITED BY
John Valasek
Texas A&M University
College Station, Texas
Volume 241
Progress in Astronautics and Aeronautics
Timothy C. Lieuwen, Interim Editor-in-Chief
Georgia Institute of Technology
Atlanta, Georgia
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PREFACE
Intelligentsystemsareentitiesthatinteractwiththeirenvironmentinsuchaway
astoachievetheirgoals.Intelligentandautonomousagentsorentitiesthathandle
decisionandcontrolfunctionsareresponsibleforensuringthatsystemsperform
properly and meet prescribed performance objectives. Consequently, the safe,
reliable, and efficient operation of these agents or entities is essential in the
domainofaerospacesystems.Althoughnoformalandwidelyaccepteddefinition
exists of what exactly makes an aerospace system intelligent and autonomous,
some combination ofthe following characteristics is likely to be inherent:
1. Learning: having goals and sensing its environment it learns, for each
system stateor situation, which action permits it to achieve its goals
2. Reasoning:introspection/havingamodelofself/managinginternalstates
3. Deliberating/planning rather than simply reacting: continually acting,
mentally and externally, and by acting reaches its objectives more often
than pure chance indicates (normally much more often)
4. Adaptability: altering the state of the system such as its location in the
environment or itsinternal state
5. Robustness toenvironment: generalizingappropriatelytonewsituations
and improving behavior given experience, including when the environ-
ment is nondeterministic, incompletely observable, and changing over
time
6. Improving efficiency (over time and/or space): producing sequences of
jointly optimized actions
7. Information compression: processing data to generate knowledge
8. Metabolization: consuming energy or resources and using it for its
internal processes and inorder toact
Researchadvancesholdthepromisetotransformaerospacewithsystemsthat
respond more quickly (e.g., autonomous collision avoidance, online path plan-
ning); work in dangerous or inaccessible environments (e.g., autonomous
systems for space exploration); provide large-scale, distributed coordination
(e.g.,automatedairtrafficcontrol,cooperativesearchandrescue);arehighlyeffi-
cient(e.g.,morphingairvehicles,morphingspaceantennas);andaugmenthuman
capabilities (e.g., next-generation glass cockpits, automated crew advisory
systems). All of these capabilities will be realized by embedding computational
intelligence,communication,control,andnewmechanismsforsensing,actuation,
and adaptation into air and space vehicles, propulsion systems, exploration
systems, and vehicle management systems toname but a few.
This book seeks to provide both the aerospace researcher and the practicing
aerospace engineer with an exposition on the latest innovative methods and
approaches that focuses on intelligent and autonomous aerospace systems. The
chapters are written by leading researchers in this field and include ideas,
xv
xvi PREFACE
directions,andrecentresultsoncurrentintelligentaerospaceresearchissueswith
afocusondynamicsandcontrol,systemsengineering,andaerospacedesign.The
content on uncertainties, modeling of large and highly nonlinear complex
systems, robustness, and adaptivity are intended to be useful in the subsystem
and the overall system level design and analysis of various aerospace vehicles.
A broad spectrum of methods and approaches is presented, including bio-
inspiration; fuzzy logic; genetic algorithms; Q-learning; Markov decision
processes; approximate dynamic programming; artificial neural networks; prob-
abilisticmaps;multi-agentsystems;andKalman,particle,andconfidencefiltering.
Althoughanoriginalwork,thisbookhasitsrootsinasimilarbookwrittenby
EwaldHeerandHenryLumfrom1989titledMachineIntelligenceandAutonomy
forAerospaceSystemsandpublishedbyAIAAintheProgressinAeronauticsand
AstronauticsSeries.Itisenlighteningtoreviewthescopeandcontentofthatwork
toseehowthefieldhasprogressedinthelastquarter-century.Thisworkfocused
exclusively on spacecraft that were currently anticipated from contemporary
requirements and applications on future space missions. It was envisioned that
these would present building blocks for further development and advances.
Machine intelligence and human–machine interaction aspects were emphasized
asameanstoincreasethetechnicalfeasibilityandeconomicfeasibilityofspace-
craftsystems.Asdefinedinthatwork,theseaspectsdescribedsystemsthataccept
human commands, reason, and perform manipulativetasks.
It is interesting that expert systems and knowledge-based systems were fea-
turedsoprominentlyinthisearlierworkbecausetheydidnotseenearlythewide-
spread use that was envisioned for them. On the other hand, information
acquisition for single and multisensor systems was identified as an important
emerging area, and this is certainly true today andfor theforeseeablefuture.
A rough indicator of the level of maturation of the intelligent systems of a
quarter-centuryagocanbeinferredfromthefactthatonlyoneofthe12chapters
of the book featured a system or application that used closed-loop intelligent or
autonomouscontrol.Theapplication was thecontrolofadeformablespacecraft
system.Bycontrast,sevenofthe12chaptersofthisbookfeatureclosed-loopintel-
ligent or autonomous control. It is clear that the field has matured to a level in
which closed-loop control is increasingly pervasive. Whether or not this trend
continues remains to be seen.
Thisbookisorganizedbroadlyintothreeparts.Althoughmanydifferentcri-
teriacouldhavebeenusedtogroupthechapters,applyingageneralformofthe
technique of classifiers eventually produced the good but by no means unique
organization used. In spite of this, many of the chapters have distinct technical
elements that could have easily justified their inclusion in a different part. This
serves to remind one of the interdisciplinary nature of intelligent systems.
Part I is titled Intelligent Flight Control and presents work on various control
techniques applied to flapping micro air vehicles, unmanned helicopters, and
generictransportaircraft.PartIIistitledIntelligentPropulsionandHealthMan-
agementandfeaturesarchitecturesforintegratedhealthmanagementandhealth
PREFACE xvii
monitoring,aswellastwochaptersonpropulsioncontrol.PartIIIistitledIntel-
ligent Planning and Multi-Agent Systems and addresses a variety of methods to
support and conduct missions ranging from autonomous soaring to cooperative
teams of UAVs to space exploration missions to air vehicle search and target
tracking.
Itismysincerehopethatthereaderfindsthebookasusefulandrewardingas
it has been writing it.
John Valasek
CollegeStation,Texas
June2012
TABLE OF CONTENTS
Acknowledgment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiii
Preface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xv
PART I INTELLIGENT FLIGHT CONTROL
Chapter 1 Towards Bio-Inspired Robotic Aircraft:
Control Experimentson Flappingand Gliding Flight . . . . . . . . . . . 1
MichaelDorothy,AdityaA.Paranjape,P.DanielKuangandSoon-JoChung,
UniversityofIllinoisatUrbana–Champaign,Urbana,Illinois
I. Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
II. Biologically Inspired CPG Control Basics . . . . . . . . . . . . . . . . . . . . . . . 4
III. Kinematics and Unsteady Aerodynamics. . . . . . . . . . . . . . . . . . . . . . . 7
IV. CPG-Based Control Results of RoboBat . . . . . . . . . . . . . . . . . . . . . . . 11
V. Flight Mechanics of MAV with Articulated Wings . . . . . . . . . . . . . . . 14
VI. Control Law Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
VII. Experiments on theElements of Perching . . . . . . . . . . . . . . . . . . . . 21
VIII. Conclusion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
Chapter 2 Neural Network-Based Optimal Control of an
Unmanned Helicopter. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
DavidNodlandandH.Zargarzadeh,MissouriUniversityofScienceandTechnology,Rolla,
Missouri;ArpitaGhosh,NationalMetallurgicalLaboratory,Jamshedpur,India;
andS.Jagannathan,MissouriUniversityofScienceandTechnology,Rolla,Missouri
I. Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
II. Helicopter Dynamic Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
III. Nonlinear Optimal Regulation and Tracking of
theHelicopter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39
IV. Simulation Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52
V. Conclusion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56
Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56
vii
viii TABLEOFCONTENTS
Chapter 3 Intelligent ConstrainedOptimal Control of
Aerospace Vehicles with Model Uncertainties . . . . . . . . . . . . . . . . . . 59
JieDingandS.N.Balakrishnan,MissouriUniversityofScienceandTechnology,
Rolla,Missouri
Nomenclature . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59
I. Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60
II. Approximate Dynamic Programming . . . . . . . . . . . . . . . . . . . . . . . . 63
III. J-SNAC Synthesis with Nonquadratic Cost . . . . . . . . . . . . . . . . . . . . 65
IV. Dynamic Reoptimization of J-SNAC Controller . . . . . . . . . . . . . . . . . 67
V. Tracking Problem withInput Constraint . . . . . . . . . . . . . . . . . . . . . . 69
VI. Numerical Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70
VII. Conclusion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84
Appendix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85
Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87
Chapter 4 Modified Reference Model MRAC (M-MRAC):
AnApplication to a Generic Transport Aircraft . . . . . . . . . . . . . . . . . 91
VahramStepanyanandKalmanjeKrishnakumar,NASAAmesResearchCenter,
MoffettField,California
I. Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91
II. Preliminaries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93
III. Reference Model Modification. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98
IV. Asymptotic Properties of M-MRAC . . . . . . . . . . . . . . . . . . . . . . . . . 100
V. Transient Properties of M-MRAC . . . . . . . . . . . . . . . . . . . . . . . . . . . 101
VI. Disturbance Rejection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 108
VII. Scalar Example. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111
VIII. Aerospace Application. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 117
IX. Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 126
Chapter 5 L Adaptive Control inFlight. . . . . . . . . . . . . . . . . . . . 129
1
EnricXargayandNairaHovakimyan,UniversityofIllinoisatUrbana–Champaign,
Urbana,Illinois;VladimirDobrokhodovandIsaacKaminer,Naval
PostgraduateSchool,Monterey,California;ChengyuCao,Universityof
Connecticut,Storrs,Connecticut;andIreneM.Gregory,NASALangley
ResearchCenter,Hampton,Virginia
I. Fast Adaptation: The Keyto Safe Flight. . . . . . . . . . . . . . . . . . . . . . 129
II. L Adaptive Control for the NPS Autonomous UAV . . . . . . . . . . . . 132
1
III. L Adaptive Control for the NASA AirSTAR Flight-TestVehicle. . . . 140
1
TABLEOFCONTENTS ix
IV. Concluding Remarks and Future Research Directions . . . . . . . . . . . 168
Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 169
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 169
PART II INTELLIGENT PROPULSION AND HEALTH MANAGEMENT
Chapter 6 Integrated Systems Health Management for
Intelligent Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 173
FernandoFigueroa,NASAStennisSpaceCenter,MississippiandKevinMelcher,
NASAJohnH.GlennResearchCenteratLewisField,Cleveland,Ohio
Acronyms. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 173
I. Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 174
II. iISHMCapability Development . . . . . . . . . . . . . . . . . . . . . . . . . . . . 175
III. iISHMin Systems Design, Integration,
and Engineering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 189
IV. Intelligent Control for iISHM-Enabled Systems . . . . . . . . . . . . . . . . 191
V. Opportunities and Needfor Advances in Verification
and Validation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 192
VI. Implementation Example:Rocket-Engine Test Facility and
Test Article. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 192
VII. Conclusion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 197
Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 198
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 198
Chapter 7 Intelligent Propulsion Control and
Health Management . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 201
SanjayGarg,NASAJohnH.GlennResearchCenteratLewisField,Cleveland,Ohio
I. Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 201
II. Turbofan Engine Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 202
III. State of the Art of EngineControl. . . . . . . . . . . . . . . . . . . . . . . . . . 204
IV. Some Retrofit Intelligent EngineControl Concepts . . . . . . . . . . . . . 209
V. Model-Based Control and Diagnostics. . . . . . . . . . . . . . . . . . . . . . . 216
VI. Conclusion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 226
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 227
Chapter 8 Genetic Fuzzy Controller for a Gas-Turbine
Fuel System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 229
AndrewVickandKellyCohen,UniversityofCincinnati,Cincinnati,Ohio
I. Introduction toGenetic Fuzzy Systems . . . . . . . . . . . . . . . . . . . . . . 229
x TABLEOFCONTENTS
II. Gas-Turbine Fuel System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 246
III. Genetic Fuzzy Gas-Turbine Fuel System . . . . . . . . . . . . . . . . . . . . . 248
IV. Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 269
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 271
PART III INTELLIGENT PLANNING AND MULTI-AGENT SYSTEMS
Chapter 9 Multiresolution State-Space Discretization
Method for Q-Learning for One or More Regions
ofInterest. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 273
AmandaLamptonandJohnValasek,TexasA&MUniversity,CollegeStation,Texas
I. Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 273
II. Reinforcement Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 276
III. Learning on a Two- and N-Dimensional Continuous Domain . . . . . 281
IV. Multiresolution State-SpaceDiscretization (AAG) for
NDimensions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 283
V. Multiple Goal Regions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 285
VI. Policy Comparison. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 286
VII. Benchmark Dynamic System Example —Inverted Pendulum . . . . 288
VIII. Autonomous Soaring Updraft/Thermal Example . . . . . . . . . . . . . . 298
IX. Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 306
Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 307
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 307
Chapter 10 Motion Planning Under Uncertainty . . . . . . . . . . . . . . 309
Ali-AkbarAgha-Mohammadi,TexasA&MUniversity,CollegeStation,Texas;
SandipKumar,Mathworks,Natick,Massachusetts;andSumanChakravorty,
TexasA&MUniversity,CollegeStation,Texas
I. Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 309
II. Mathematical Formulation forDecision Making
Under Uncertainty . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 318
III. Generic Framework for Sampling-Based Feedback
Motion Planners . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 322
IV. Sampling-Based Feedback Motion Planners with
Process Uncertainty and Stochastic Maps . . . . . . . . . . . . . . . . . . . . 330
V. Sampling-Based Feedback Motion Planners with Motion
Uncertainty and Imperfect State Information . . . . . . . . . . . . . . . . . 345
Appendix A: Proof of Lemma4 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 374
Appendix B: Proof of Corollary 1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 376
Appendix C: Proof of Proposition 1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 376
TABLEOFCONTENTS xi
Appendix D: Proof of Theorem 1. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 380
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 381
Chapter 11 Protocol Utilization in Intelligent Systems to
Facilitate Exploration Missions . . . . . . . . . . . . . . . . . . . . . . . . . . . . 387
M.LyellandW.Drozd,IntelligentAutomation,Inc.,Rockville,Maryland;
A.GrinbergWebb,MontgomeryCollege,Rockville,Maryland;and
J.NandaandW.Chen,IntelligentAutomation,Inc.,Rockville,Maryland
I. Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 387
II. Mission Criteria and Behavior Norms: The Impact on
Framework Design. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 390
III. Technology Fundamentals: Software AgentParadigm
and Protocols. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 394
IV. Multi-Agent-Based Subsystems and Astronaut Interactions
and Oversight: Behavior and Protocol Development . . . . . . . . . . . 395
V. Robot Agent . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 407
VI. Autonomous Robot Teams and Astronaut Oversight:
Protocol Development. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 411
VII. Protocol Utilization by theDeployedRobot Team . . . . . . . . . . . . . 428
VIII. Discussion and Conclusions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 438
Acknowledgment. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 441
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 441
Chapter 12 Frameworkfor User–Guided Search and
Adaptive Target Trackingvia Cooperative UAVs . . . . . . . . . . . . . . . 445
R.A.Cortez,D.TolicandI.Palunko,UniversityofNewMexico,Albuquerque,
NewMexico;N.Eskandari,UniversityofBritishColumbia,Vancouver,British
Columbia,Canada;andM.Oishi,R.FierroandI.Wood,UniversityofNewMexico,
Albuquerque,NewMexico
I. Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 445
II. Problem Formulation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 447
III. UAV Coordination . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 449
IV. Quadrotor Baseline Controller Design . . . . . . . . . . . . . . . . . . . . . . . 456
V. Simulations and Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 460
VI. Conclusions and Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 465
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 467
Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 469
Supporting Materials . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 491