air force office of scientific research dynamics & control program overview ltcol scott wells,...

Air Force Office of Scientific ResearchDynamics & Control Program Overview

LtCol Scott Wells, PhDProgram Manager

AFOSR/ND

Air Force Research Laboratory

2

Introduction

• AFOSR Overview

• Portfolio Overview

– Management Summary

– “Technical” Summary

• Future Direction/ Ideas

• Conclusions

Introduction

AFOSR Overview

Portfolio Overview

• Management Summary

•Technical Summary

Future Direction/ Ideas

Conclusions

3

Introduction

AFOSR Overview

Portfolio Overview




Conclusions

AFOSR Mission

Expand the horizon of scientific knowledge through leadership and management of the Air Force’s basic research program by investing in basic research efforts in relevant scientific areas.

Central to AFOSR’s strategy is the transfer of the fruits of basic research to industry, the academic community, and to the other technical directorates of AFRL.

Creating revolutionary

scientific breakthrough

s for the Air

Force

Creating revolutionary

scientific breakthrough

s for the Air

Force

Check out www.afosr.af.mil

http://www.afosr.af.mil/

4

OrganizationAir Force Office of Scientific Research

DIRECTOR

Dr. Brendan Godfrey

DEPUTY DIRECTOR

Col. Michael Hatfield

CHIEF SCIENTIST

Dr. Thomas W. Hussey

SENIOR RESIVIST

Lt. Col. Joe Fraundorfer

PHYSICS AND ELECTRONICS

Dr. Don Carrick

AEROSPACE & MATERIALS SCIENCES

Dr. Thomas Russell

MATHEMATICS, INFORMATION & LIFE SCIENCES

Dr. Genevieve Haddad

INTERNATIONAL OFFICEDr. Mark Maurice

ASIAN OFFICE OF AEROSPACE R&D

Dr. Ken Goretta

EUROPEAN OFFICE OF AEROSPACE R & D

Col. Stephen Pluntze

HUMAN RESOURCES

Ms. Terry Hodges

STAFF JUDGEADVOCATE

Maj. Michael Greene

DIRECTORATE OF POLICY AND INTERGRATION

Maj Ryan Umstattd

DIRECTOR OF CONTRACTING

Ms. Trish Voss

Introduction

AFOSR Overview

Portfolio Overview

•Management Summary



Conclusions

5

AFOSR Research Areas

Aerospace & Materials Sciences

Mathematics, Information & Life

Sciences

Physics & Electronics

Areas of Enhanced Emphasis- Information Sciences - Novel Energy Technology

- Mixed-Initiative Decision Making - Micro Air Vehicles

- Adversarial Behavior Modeling - Nanotechnology

• Physics• Electronics• Space Sciences• Applied Math

• Structural Mechanics• Materials• Chemistry• Fluid Mechanics• Propulsion

• Info Sciences• Human Cognition• Mathematics• Bio Sciences

Introduction

AFOSR Overview

Portfolio Overview




Conclusions

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AFOSR Supports Basic Research

AFOSR

Asian Office of Aerospace

Research and Development

European Office of Aerospace

Research and Development

728 Research grants at 211 universities

194 Research projects at AFRL

186 STTR contracts

39 Postdocs at AFRL

90 Summer Faculty at AFRL

264 Short-term foreign visitors

37 Personnel exchanges

58 Technical workshops

205 Conferences sponsored

Foster Revolutionary Basic Research

Build Relationships

Southern Office of Aerospace

Research and DevelopmentNew in May 07

Basic Research Funding

Introduction

AFOSR Overview

Portfolio Overview




Conclusions

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FY07 AF Core Basic Research Investment By Discipline

Solid Mechanics and Structures

7%

Chemistry13%

Materials8%

Fluid Mechanics5%

Mathematics and Computer Sciences

13%Propulsion7%

Space and Information Sciences

11%

Biological Sciences4%

Human Performance4%

Physics11%

External Research Programs Interface

3%

Electronics14%

Introduction

AFOSR Overview

Portfolio Overview




Conclusions

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NAME: Scott Wells YEARS AS AFOSR PM: 8 months

BRIEF DESCRIPTION OF PORTFOLIODeveloping theory, algorithms, and tools for reliable, practical design and analysis of high performance robust and adaptive control laws for future AF systems operating in uncertain, complex, and adversarial environments

SUB-AREAS IN PORTFOLIOControl and dynamics of Unmanned Aerial Vehicles (single/multiple agent)

• Autonomous Single Agent/Enabling Technologies• Cooperative Multiple Agent

Aerodynamic flow control and control of unsteady phenomenaActive waveform controlDynamics and Modeling (modeling, identification and uncertainty characterization)General control theory (nonlinear, adaptive, hybrid)Validation & Verification (V&V)

PORTFOLIO OVERVIEWDynamics & Controls

66

71

64

60

65

70

75

2005 2006 2007

Total Number of Projects (all sources)

Introduction

AFOSR Overview

Portfolio Overview




Conclusions

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37%

13%3%

11%

20%4% 12%

Autonomous UAV

Cooperative UAVFlow Control

Active Waveform Control

Dynamics/Modeling

General Control TheoryV&V

Sub-Area Distribution(Includes FY06 & FY07 Projects)

0

1

2

3

4

5

6

7

8

9Vision Based Control

Integrated G&C

Trajectory/Waypoint Tracking

0

5

10

15

20

25

30

Mixed Initiative

Information Theory

Network Theory/Architecture

State Estimation

Tracking

Path Planning

Task Allocation

Autonomous UAV

Cooperative UAV


Introduction

AFOSR Overview

Portfolio Overview




Conclusions

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Funding Elements

Extramural, 26%

Intramural, 17%

MURI, 29%

STTR, 20%

Themes, 3%

Other, 1%

PECASE, 0%

YIP, 0.4%

HBCU, 0%

DEPSCoR, 0%

DURIP, 0%

DARPA, 0%

CCCS, 4%

Introduction

AFOSR Overview

Portfolio Overview




Conclusions

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PORTFOLIO OVERVIEWTechnical Summary

• Control and dynamics of Unmanned Aerial Vehicles (single/multiple agent)

– Autonomous Single Agent/Enabling Technologies

– Cooperative Multiple Agent

• Aerodynamic flow control and control of unsteady phenomena

• Active waveform control

• Dynamics and Modeling (modeling, identification and uncertainty characterization)

• General control theory (nonlinear, adaptive, hybrid)

• Validation & Verification (V&V)

Introduction

AFOSR Overview

Portfolio Overview




Conclusions

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• Dynamics & Autonomous UAV Control

• Cooperative Multi-agent Dynamics and Control

Introduction

AFOSR Overview

Portfolio Overview


• Technical Summary


Conclusions

Unmanned Aerial Vehicles (UAV)

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• Dynamics & Autonomous UAV Control (single agent/enabling capabilities)

– Trajectory/waypoint tracking

– Target tracking

– Collision and obstacle avoidance

– Integrated Guidance and Control (2)

– Vision-based control (6)

• Active Contours

• Optic Flow

• Dynamic Feature Extraction

Introduction

AFOSR Overview

Portfolio Overview




Conclusions

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FY03 MURI: Active-Vision Control Systems forComplex Adversarial 3-D Environments

Participants

• Georgia Tech: E. Johnson, UCLA, MIT, VT

Objective Develop methods that utilize 2-D and 3-D imagery to enable aerial vehicles to autonomously detect and prosecute targets in uncertain complex 3-D adversarial environments -- without relying on highly accurate 3-D models of the environment

• Air and ground vehicle tracking

–Particle filtering + curve evolution to estimate the contour position and velocity for moving and deforming object

–Optimal guidance policies for observability, including intelligent excitation

–Integrated estimation/guidance with a composite adaptation approach

• Obstacle/hazard avoidance

–Layered active appearance models

–Optical matting (separate back/foreground)

–Guidance and estimation for obstacle avoidance

• Vision to replace traditional sensors

–Vision-only flight control

–Vision aided approach and landing

–Vision-aided inertial navigation

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Cooperative Multi-agent Dynamics and Control

– Task Allocation (2)

– Path Planning (11)

– Tracking (3)

– State Estimation (2)

– Network Theory/Architecture (6)

– Information Theory (2)

– Mixed Initiative (2)


Decisions/Decisions/ComputationComputation

Cen

tral

ized

Dec

entr

aliz

ed

Task AllocationPath PlanningTarget Tracking

Task AllocationPath PlanningTarget Tracking

Introduction

AFOSR Overview

Portfolio Overview




Conclusions

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Approach: Dimensions of Cooperative Control

• Distributed control and computation• Vehicle flocking with obstacle avoidance• Optimal navigation in partially known environments

• Adversarial interactions• Probabilistic differential games

• Mixed integer LP methods

• Uncertain evolution• Probability maps with moving opponents

• Complexity management• Decomposition methods for hierarchical planning

• Experimentation: • Case Study Simulations + Hybrid Hardware Realization

Goal:

Deployment of Large Scale Networks of (semi) Autonomous Vehicles

FY01 MURI Cooperative Control of Distributed Autonomous Vehicles

in Adversarial Environments

Participants

UCLA: J. Shamma, MIT, Caltech, Cornell

Complex Collective Behavior from Simple Individual Behavior

Sample Result

Random rewiring of links with probability p increases performance of consensus algorithms and distributed filtering 1000 times

Introduction

AFOSR Overview

Portfolio Overview




Conclusions

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Accomplishments• Deployment Algorithms

• Provable guarantees for coverage

• New rigorous target servicing

• Verification and validation• Switching for stochastic hybrid systems

• Verification via learning and randomization

• Control-oriented communication & information theory

• Channel capacity theorem for control

• Delay adaptive routing protocols

FY02 MURICooperative Networked Control of Dynamical

Peer-to-Peer Vehicle Systems

Objective• Establish theory, scalable algorithms and

distributed protocols for achievable global performance in cooperative networked control.

• Verify robustness to: uncertainty, malicious attacks, rapidly evolving mission objectives

Scientific Approach • Scalable algorithms for verification of multi-

vehicle systems

• Languages for real-time networked vehicle interaction

• Theory for information management in distributed feedback systems

• Algorithms for allocations based on spatial geometry

Participants• UIUC: G. Dullerud, MIT, Stanford

Control & Information

Theory

Computing & Verification

Communications

AutonomousVehicles

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• Waiting for competition results to be released.

FY07 MURIBehavior of Systems with Humans and

Unmanned Vehicles

Introduction

AFOSR Overview

Portfolio Overview




Conclusions

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• Lower order modeling (4)

• Control schemes (4)

– Classical, optimal

– Adaptive

• Sensor/Actuator placement (1)

– Controllability/Observability issues

• Actuators

– Synthetic jets, surface deflection, plasma

Aerodynamic flow control and control of unsteady phenomena

Introduction

AFOSR Overview

Portfolio Overview




Conclusions

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Active waveform control (2)

• Control of electromagnetic surface properties

• Control of deformable mirrors and beam control

Introduction

AFOSR Overview

Portfolio Overview




Conclusions

Input Amplitude

Input Phase

Output Intensity

Output Phase

Initial Output

Simulation ExperimentOutput Intensity

Output Phase

Final Output

Desired OutputInitial Output

Output Amplitude

Output Phase

Final Output

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Dynamics and Modeling

• System Modeling (7)

– Wave dynamics for engines

– Atomic scale processes, Quantum control theory

• System Identification (1)

• Uncertainty Characterization (1)

Introduction

AFOSR Overview

Portfolio Overview




Conclusions

thermoacousticsflutter

AcousticsStructures

Combustion Fluid Dynamics

F(p,) + a2()p

Wave Speed Mistuning

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General control theory

• Adaptive Control (5)

• Nonlinear Control (2)

• Hybrid Control (2)

• Other (4)

Introduction

AFOSR Overview

Portfolio Overview




Conclusions

Control

AdaptiveEstimation

Nonlinear Sytemwith Unmodeled

Dynamics

SensorsMeasurements

StateEstimates

ControlInputs

AdaptiveControl

AdaptiveEstimation

NeuralNetworks

NeuralNetworks

Specifies desired closed-loop dynamics

Reference Model

SLG50048526-005.ppt

Robust Baseline Autopilot

Command

Tracking Error

Adaptive/LearningProcess

Desired Response

ActualResponse

Nonlinear AdaptiveAugmentation

Adaptive Flight Control System = Robust Baseline Autopilot + Nonlinear Adaptive Augmentation

d x

Tc d d rx t A x t B g u x B r t

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Validation & Verification (3)Introduction

AFOSR Overview

Portfolio Overview




Conclusions • The nominal controller Knom is the LQR optimal feedback controller with double precision floating-point coefficients.• Admissible controllers C are controllers that yield an LQR cost that is, at most, 15% suboptimal.• The complexity measure Ф is the number of bits required to express K.• The best design, which is 14.9% suboptimal, gives only 1.5 bits/coefficients.

Simulink/Stateflow

Metamodels

ComponentModel

PlatformTopology

PlatformMapping

Simulink/StateflowControl Design

+

=TargetCode

For RTSystem

Configuration Files

RT schedules

Analysis Files

Verification Models

Toolchain

• Example control design problem:

Model Certificates

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Objective Develop new approaches to designing/developing distributed embedded systems to inherently promote high confidence, as opposed to design-then-test approaches as prescribed by the current V&V process

Scientific Approach• Formal reasoning about distributed,

dynamic feedback systems• Relationships between test coverage and

system properties• Architectures to provide behavior

guarantees of *online* V&V• V&V aware architectures• Multi-threaded control• Approximate V&V

FY06 MURI: High Confidence Design forDistributed, Embedded Systems

GoalNew feedback-based approaches to embedded systems that are designed around V&V

Figure 1 – Exponential Growth of Flight-Safety-Critical

Systems Is Expected due Primarily to Autonomy

Frameworks and Tools for High-Confidence Design of Adaptive, Distributed Embedded Control Systems

Specification, Design and Verification of Distributed Embedded Systems

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Future Directions/ Ideas

• Big problems that need work

– Validation & Verification

– Mixed Initiative Cooperative Control

– Network & Information Theory in Controls

– Dynamics/Modeling/Uncertainty

• dynamic data dimensionality reduction techniques

• classification of dynamical models of high-dimension

• stochastic modeling of non-stationary dynamics

– Future Directions Report (Spring 07)

Introduction

AFOSR Overview

Portfolio Overview




Conclusions

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Conclusions and Future Directions

• Cohesive and well-connected program with national leadership, scientific innovations & technology transitions

– Honors and accomplishments reflect research quality

• 13 professional society fellows

• 2 NAE members

• 3 AFOSR star teams

• Robocup F180 class world champions, 2003/2002/2000/1999

• 9 NSF career awards

• PECASE award winners in 2000, 2002

• 180+ reported refereed journal articles

– A priori consideration of practical application aids opportunistic technology transition

• Future directions in Dynamics & Control

– Continue to pursue scientific advances in control for high risk, long range multidisciplinary and unconventional applications

Introduction

AFOSR Overview

Portfolio Overview




Conclusions

Backup slides

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•Reconfigurable Adaptive Flight Control with Limited Authority Actuation

•Adaptive autopilot augmentation designs (tech transitions):

•L-JDAM, (Laser guided MK-82 JDAM)

– Demonstrated (in flight) fast adaptation to unknown aerodynamics

– 2 successful flight tests at Eglin AFB

• November 2004: canned

• January 2005: guided, stationary target

– Summer 2006: successful flight tests, moving target

Adaptive Flight Control Transitions

Some Research HighlightsAdaptive Flight Control in L-JDAM

Introduction

AFOSR Overview

Portfolio Overview



Some Research Highlights


Conclusions

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Some Research HighlightsFirst-ever Vision Guided Autonomous Formation Flight

Johnson, Georgia Tech, UCLA, MIT, VTIntroduction

AFOSR Overview

Portfolio Overview





Conclusions

31

Introduction

Portfolio Overview



Trends in Dynamics & Control



Conclusions

Some Research HighlightsVision Guided Autonomous Tracking of Ground Vehicle

Beard/McLain, Brigham Young

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Introduction

Portfolio Overview



Trends in Dynamics & Control



Conclusions

Some Research HighlightsCoordinated Autonomous Tracking/ Indoor flight lab

How, MIT

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Discovery Challenge Thrusts (DCT)

• Systems and Networks

• Integrated Sensors, Algorithmic Processors & Interpreters (I-ATR)

• Radiant Energy Delivery and Materials Interactions

• Thermal Transport Phenomena and Scaling Laws

• Super-Configurable Multifunctional Structures

• Robust Decision Making

• Self-Reconfigurable Electronic/Photonic Materials & Devices

• Socio-Cultural Prediction

• Turbulence Control & Implications

• Space Situational Awareness

• Devices, Components, and Systems Prognosis

Introduction

AFOSR Overview

Portfolio Overview




Conclusions

34


Jan

Jun

Dec

Oct

Projected Grant Start Date, 1 Dec

Beginning of Fiscal Year, 1 Oct

Practical Deadline for Proposals, 1 Jun

White Papers/Proposal Prep

ExternalReviews

Funding Decisions

Note: AFOSR BAA is continuously open. Proposals can always be submitted at any time. However, in practice there is a timeline.

Note: AFOSR BAA is continuously open. Proposals can always be submitted at any time. However, in practice there is a timeline.

Funding TimelineIntroduction

AFOSR Overview

Portfolio Overview




Conclusions

air force office of scientific research dynamics & control program overview ltcol scott wells,...

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