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Dynamics and Control15 March 2011
Dr. Fariba Fahroo
Program Manager
AFOSR/RSL
Air Force Office of Scientific Research
AFOSR
Distribution A: Approved for public release; distribution is unlimited. 88ABW-2011-0775
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2011 AFOSR SPRING REVIEW2304AX PORTFOLIO OVERVIEW
NAME: Fariba Fahroo
BRIEF DESCRIPTION OF PORTFOLIO:
Developing mathematical theory and algorithms based on the
interplay of dynamical systems and control theories with the aim ofdeveloping innovative synergistic strategies for the design, analysis,and control of AF systems operating in uncertain, complex, andadversarial environments.
LIST SUB-AREAS IN PORTFOLIO:General Control Theory: Adaptive Control, Hybrid Control, StochasticControl, Nonlinear Control theory
Distributed Control: Stochastic and AdversarialV&V of Complex SystemsControl of Complex Networks
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Dynamics & ControlAn Overall Picture
A basic feedback loop of Sensing, Computation, and Actuationis the
central concept in control. Feedback occurs in nature, hence the link of control theory to physical
sciences and biology. In engineered systems it provides regulation andstabilization, shaping the systems behavior. It deals with uncertainty in
dynamics, inaccurate measurement, variability of components anddisturbances.
Modern control theory is everywhere: Aircraft/Weapon Systems,Autonomous Vehicles, Robotics, Turbo machinery, Aerodynamic Flow,Dynamic Structures, Adaptive Optics, Satellites, Information Systems,etc.
New challenging trends in Control ---confluence of control, computing,and communication
Complex networked system
Sensor and data rich systems
V&V of complex systems
AUTONOMOUSsystems
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Key Technology Areas forAutonomous Systems
Autonomy from the Dynamics and Control Point of View
Systems
Trusted, Adaptive, Flexibly Autonomous Systems
V&V for Complex Adaptive Systems
Intelligence
Autonomous Reasoning and Learning
Resilient Autonomy
Autonomous Mission Planning
Decision Support Tools
Networks
Complex Adaptive Distributed Networks from a single agent
control to control of multi-agent distributed, heterogeneousnetworks
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Control Sub-disciplines
General Control Theory for complex missions in uncertain, constrainedenvironments
Robust, Adaptive Control uncertain parameters, unmodeled dynamics
L1- adaptive control laws with communication constraints
Nonlinear Control numerics for optimal control and games
Hybrid Control interaction of discrete planning algorithms and continuousprocesses stability results for the rich dynamics of these systems
Stochastic control --- uncertainty in the dynamics (noise) and the controllersuse of fractional Brownian motion
Challenging Areas
Vision-Based Control Game theory in the context of human-machine networks
Quantum Systems and control
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Control Sub-disciplines
Distributed , Cooperative Control of Autonomous Multiple-Agents
for complex tasks in uncertain, adversarial environments (information theory, information fusion, network theory, robustdecision making)
Less emphasis on deterministic cooperative control and pathplanning
More emphasis on stochastics and adversarial modeling inautonomous and cooperative control
Special topics in MURIs
Verification & Validation for Distributed Embedded Systems (Dynamics & Control and Software & Systems)
Mixed Initiative/HMI (Dynamics & Control and Robust DecisionMaking)
Distributed Learning and Information Dynamics in NetworkedAutonomous Systems
Human-Machine Adversarial Networks
P hi d C ll b i
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Partnerships and CollaborationsIntramural Activities
AFRL/RBCA --- Control Sciences Center of Excellence
Siva Banda -- Autonomous and Cooperative Control of Air Vehicles MAACS at
Univ of Michigan
David Doman -- Dynamics and Control of Minimally Actuated Biomimetic Micro-Robotic Aircraft with Insect-like Maneuverability
Derek Kingston -- Mission Management for Cooperative Heterogeneous Systems
in Dynamically Changing EnvironmentsCorey Schumacher -- Operator-UAV Decision-Aiding
AFRL/RV --- Space Vehicles
Seth Lacy -- Uncertainty Accommodating Control
Pham Khan -- Self-Knowledge, Coalition, and Learning for Decision-Making: Performance Robustness against Uncertain
AFRL/RD --- Darryl Sanchez -- The Collective Control of Multiple DeformableMirrors for Use in Compensating Light Propagating through Deep Turbulence
New Tasks starting this year in Space Vehicles, Munitions, and Human Effectiveness
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External Collaborations
Close coordination with other funding Agencies --- PBD709 Science of
AutonomyNSF: Misawa, Baheti, Horn
ARO: Zachery, Chang, Iyer, Dai
ONR: Steinberg, Kamgar-Parsi
Increasing overlaps in many areas at the basic research level Across
DoD funding agencies focus on platform dependent applications is
decreasing.
Dynamics, Distributed Control, Network Theory, Game Theory, Cognition,
Human-Machine Interface
The program is unique among the DoD agencies, since it is disciplinebased, not application, or programmatic based --- focus area in flight.
AF niche areas: Support of mini-programs in Game Theory,
Computational nonlinear Control, Distributed Control, V&V
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Robust Fast Adaptation: L1 Adaptive Control
Naira Hovakimyan (UIUC)
Control law objectives:
Keep aircraft in the wind tunnel data
envelope (accurate models)
Eventually, return to normal flightenvelope
Predictable :: Repeatable :: Testable :: Safe
Failure ofconventional
adaptive control
(limited to slow adaptation)
Is A/C controllable
here?
Control actions within 2-4 seconds of failure
onset are critical:
Need for transient performance guarantees
Predictable response
Need for fast adaptationSource: NASA
Guaranteed robustness with
fastadaptation with L1 No ain-schedulin
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L1 Adaptive Flight Control Law on AirSTAR GTM
A Transition Story Development ofhighly-maneuverable platforms with control algorithms guaranteeing desired transient
response, even in the presence offailures and platform damage.
Reduction in design cycle-time and development costs through systematic V&V design methodologies.
L1 AFCS
Single Engine Failure (100% to 0%)
Wind tunnel data
Accurate models
(low-uncertainty)
Extrapolated models
(high-uncertainty)
Is A/Ccontrollable
here?
5.5 % geometrically and
dynamically scaled model
High Angle of Attack Captures
Stick-to-Surface
High model
uncertainty
L1 AFCLStick to surface
S2S
L1 AFCL
Aggressive departure
Roll rate above 60dps
Repeatable results
Two =18deg captures
http://videos/AirSTAR%20Flt%2023%20-%20Adaptive_Ctrl_Law_Clip.wmvhttp://videos/AirSTAR%20Flt%2025%20-%20Stick_Surface_Ctrl_Clip.wmvhttp://videos/07242009_53_Mode_3.3_engine_out.xvid.avihttp://videos/07242009_51_Mode_1_engine_out.xvid.avi8/7/2019 1. Fahroo - Dynamics Control
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L1 Cooperative Control of Autonomous
Systems New paradigm for time-critical coordinated path following ofmultiple autonomous vehicles
Execution of complex missions in adverse, uncertain, heterogeneous environments Possible applications: coordinated road search, forest fire detection, coordinated ground-
target suppression, construction of marine habitat mappings
Co-funded by ONR, Collaboration with NPS
2DOF gimbal with
video camera
1DOF gimbal with
HR camera
Flight imagery of
4 consecutive frames
3D geo-referenced model of the operational
environment built from 2D HR frames(courtesy of UrbanRobotics)
I f ti Th ti S i d P di ti
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Information Theoretic Sensing and PredictiveControl
Francesco Borrelli, Karl Hedrick (UC Berkeley)
Principal objectives of the research:
Investigate cooperative active sensing based on information-theoreticnotion of optimality
Establish a framework for cooperative decision making in thepresence of uncertainties and strict constraints
Application study of interest: Cooperative Search And TrackDistributed Energy Management
Research efforts have focused on robust distributed decision makingunder hard constraints with two specific problems:
Fast consensus under state and input constraints
Decentralized robust control invariance for constrained linear andswitched linear systems
I f ti Th ti S i d
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Information Theoretic Sensing andPredictive Control
Consensus under flow constraints
Network of agents storing and exchanging resource (data, energy ) Hard constraints:
limited storage limited flow capacities of transfer links
Goal: consensus resulting in equal amount of resource stored by each agent
Main result: Distributed non-linear constrained consensus protocol agents exchange information only with neighbors exchange of resources subject to hard constraints on flow convergence provable for time-varying graphs
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Information Theoretic Sensing andPredictive Control
Francesco Borrelli, Karl Hedrick
Current industrial approach Passive dissipation only
Passive dissipation: find the cell with lowest charge MINanddissipate all remaining cells until MINisreached.
Transfer to Industry: Battery Control Management for Automotive Vehicles
Time to balance: ~96 hours (simulation above shows only first 18hrs)
Energy dissipation ~2179 [Wh]
initialSoC
MinandMaxSo
C
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Information Theoretic Sensing andPredictive Control
Francesco Borrelli, Karl Hedrick
Transfer to Industry: Battery Control Management for Automotive Vehicles
Use consensus approach Agents are Li-ion cells of a battery Goal: Fast balancing with constraint
satisfaction and minimal energy loss Standard (Mixed-integer) optimization
~18 days for control computation Developed approach: 2% suboptimal
and 2min for control computation
Time to balance improvement:
~96 hours to ~7 hours Energy dissipation improvement:~2179 [Wh] to ~200 [Wh]
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Frameworks and Tools for High-Confidence Design ofAdaptive, Distributed Embedded Control Systems
Systems
Multi-University Research Initiative on High-Confidence Design forDistributed Embedded (2006)
Team Members:
Vanderbilt: J. Sztipanovits (PI) and G. KarsaiUC Berkeley: C. Tomlin (Lead and co-PI), Edward Lee and S. SastryCMU: Bruce Krogh (Lead and co-PI) and Edmund ClarkeStanford: Stephen Boyd
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Scientific Challenges
Composition of hybrid control systems withoutneglecting attributes of computation andcommunication platforms
Correct-by-construction model-based software design
for high-confidence, networked embedded systemsapplications
Composable tool architecture that enables toolreusability in domain-specific tool chains
Testing and experimental validation
Long-Term PAYOFF:
Decrease the V&V cost of distributed embedded
control systems
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Recent Transitions
The project results are transitioned through Berkeleys Ptolemy tool
suite and Vanderbilts MIC tool suite. Both of these tool suites areopen source and widely used in industry and academia.
Ptolemy II 8.0 beta was released on February 26, 2010 The Ptolemysource tree is available via CVS. Team works with AFRL/RIEA,Extensible Modeling and Analysis Framework Project, LM/ATL Naomi
project, ARL SCOS project. Specific transitioned results are: semanticannotations, multi-modeling, various model-based code generators.
Vanderbilts MIC tool suite (GME, GReAT, UDM, OTIF) had a majorrelease in 2010. The full MURI tool suite has been integrated anddisseminated using the MIC tools.
Vanderbilt continued working with LM/ATL,GM, Raytheon, BAE Systems andBoeing research groups on transitioning model-based design technologiesinto their programs.
Major transitioning effort have started up under DARPAs META 2 program:
Design Languages (Vanderbilt- Boeing Georgia Tech)
Tool Chain (Vanderbilt Boeing Georgia Tech)
V&V (SRI International Honeywell - Vanderbilt)
Ch ll t A t
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Challenges to Autonomy:Data Deluge
Mario Sznaier (Northeastern Univ)
The Curse of dimensionality is a major roadblock in achievingflexible autonomy
Extracting actionable information from very large data setsremains a challenge --- Compressive Information Extraction
Relevant to problems of current interest in:
systems identification
computer vision
machine learning
A hidden hybrid systems identification problem
T h i l A h
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Information extraction as an identification problem:
Look for changes in the rank of the Hankel matrix, H
Model data streams as outputs of piecewise LTI systems
Interesting events Model invariant(s) (e.g. Model order)changes
Technical ApproachSznaier
u
G()y
features, pixelvalues,
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Transformational Opportunities
Video clip from the failed Times Square bombing attempt. Hankel
rank jumps identify contextually abnormal activity by the suspect
2010 MURI Topic 16 (AFOSR)
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2010 MURI Topic 16 (AFOSR)Human-Machine Adversarial Networks
(Tamer Baser UIUC)
Multi-Layer and Multi-Resolution Networks ofInteracting Agents in Adversarial Environments
Overall Goal: To develop a comprehensive multi-layer multi-resolution(MLMR) framework, with
associated theory, computational algorithms andexperimental testbeds, for dynamic games played onmultiple scalesby spatially distributed teams ofhumanand automateddecision makers, who
communicate and interact over a network and aresubject to adversarialaction.
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Vision and Overarching Goal
Complex interactions, Uncertaintyand adversarial actions, Trust, learning, Humans-in-the-loop, Information and communication, and Design of architecturesto facilitate generation andtransmission of actionable informationfor performanceimprovementunder different equilibrium solutions
In line with the Overall Goals of Flexible Autonomy