1. Fahroo - Dynamics Control

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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.avi


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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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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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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

Documents

1. Fahroo - Dynamics Control