Air Force Office of Scientific Research Dynamics & Control Program Overview LtCol Scott Wells, PhD Program Manager AFOSR/ND Air Force Research Laboratory

Introduction AFOSR Overview Portfolio Overview Management Summary Technical Summary Future Direction/ Ideas Conclusions AFOSR Overview Portfolio Overview Management Summary “Technical” Summary Future Direction/ Ideas Conclusions

Creating revolutionary scientific breakthroughs 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. Introduction AFOSR Overview Portfolio Overview Management Summary Technical Summary Future Direction/ Ideas Conclusions Creating revolutionary scientific breakthroughs for the Air Force I’d like to start off with an overview of AFOSR itself, followed by an introduction to my portfolio in D&C, and then Fariba will discuss an area highly linked to many large scale problems in dynamics and control, computational mathematics. 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. Check out www.afosr.af.mil

Air Force Office of Scientific Research Organization Air Force Office of Scientific Research Introduction AFOSR Overview Portfolio Overview Management Summary Technical Summary Future Direction/ Ideas Conclusions DIRECTOR Dr. Brendan Godfrey CHIEF SCIENTIST Dr. Thomas W. Hussey DEPUTY DIRECTOR Col. Michael Hatfield 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 OFFICE Dr. 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 DIRECTORATE OF POLICY AND INTERGRATION Maj Ryan Umstattd STAFF JUDGE ADVOCATE Maj. Michael Greene DIRECTOR OF CONTRACTING Ms. Trish Voss

AFOSR Research Areas Areas of Enhanced Emphasis Introduction AFOSR Overview Portfolio Overview Management Summary Technical Summary Future Direction/ Ideas Conclusions Mathematics, Information & Life Sciences Aerospace & Materials Sciences Physics & Electronics Structural Mechanics Materials Chemistry Fluid Mechanics Propulsion Physics Electronics Space Sciences Applied Math Info Sciences Human Cognition Mathematics Bio Sciences Areas of Enhanced Emphasis - Information Sciences - Novel Energy Technology - Mixed-Initiative Decision Making - Micro Air Vehicles - Adversarial Behavior Modeling - Nanotechnology

AFOSR Supports Basic Research Funding Foster Revolutionary Basic Research Introduction AFOSR Overview Portfolio Overview Management Summary Technical Summary Future Direction/ Ideas Conclusions 728 Research grants at 211 universities AFOSR 194 Research projects at AFRL 186 STTR contracts Build Relationships 39 Postdocs at AFRL 90 Summer Faculty at AFRL 37 Personnel exchanges Asian Office of Aerospace Research and Development 264 Short-term foreign visitors European Office of Aerospace Research and Development 58 Technical workshops 205 Conferences sponsored Southern Office of Aerospace Research and Development New in May 07

FY07 AF Core Basic Research Investment By Discipline Introduction AFOSR Overview Portfolio Overview Management Summary Technical Summary Future Direction/ Ideas Conclusions

PORTFOLIO OVERVIEW Dynamics & Controls Introduction AFOSR Overview Portfolio Overview Management Summary Technical Summary Future Direction/ Ideas Conclusions NAME: Scott Wells YEARS AS AFOSR PM: 8 months BRIEF DESCRIPTION OF PORTFOLIO Developing 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 PORTFOLIO 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)

Sub-Area Distribution (Includes FY06 & FY07 Projects) PORTFOLIO OVERVIEW Dynamics & Controls Introduction AFOSR Overview Portfolio Overview Management Summary Technical Summary Future Direction/ Ideas Conclusions Sub-Area Distribution (Includes FY06 & FY07 Projects) Autonomous UAV Cooperative UAV

PORTFOLIO OVERVIEW Dynamics & Controls Introduction AFOSR Overview Portfolio Overview Management Summary Technical Summary Future Direction/ Ideas Conclusions To do this we host a variety of programs.

PORTFOLIO OVERVIEW Technical Summary Introduction AFOSR Overview Portfolio Overview Management Summary Technical Summary Future Direction/ Ideas Conclusions 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)

PORTFOLIO OVERVIEW Technical Summary Unmanned Aerial Vehicles (UAV) Introduction AFOSR Overview Portfolio Overview Management Summary Technical Summary Future Direction/ Ideas Conclusions Dynamics & Autonomous UAV Control Cooperative Multi-agent Dynamics and Control

PORTFOLIO OVERVIEW Technical Summary Introduction AFOSR Overview Portfolio Overview Management Summary Technical Summary Future Direction/ Ideas Conclusions 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

FY03 MURI: Active-Vision Control Systems for Complex Adversarial 3-D Environments 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 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 Participants Georgia Tech: E. Johnson, UCLA, MIT, VT

PORTFOLIO OVERVIEW Technical Summary Introduction AFOSR Overview Portfolio Overview Management Summary Technical Summary Future Direction/ Ideas Conclusions 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/ Computation Centralized Decentralized Task Allocation Path Planning Target Tracking

FY01 MURI Cooperative Control of Distributed Autonomous Vehicles in Adversarial Environments Goal: Deployment of Large Scale Networks of (semi) Autonomous Vehicles 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 Introduction AFOSR Overview Portfolio Overview Management Summary Technical Summary Future Direction/ Ideas Conclusions 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 Participants UCLA: J. Shamma, MIT, Caltech, Cornell

Control & Information Theory Computing & Verification FY02 MURI Cooperative 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 Control & Information Theory Computing & Verification Communications Autonomous Vehicles 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 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 Participants UIUC: G. Dullerud, MIT, Stanford

FY07 MURI Behavior of Systems with Humans and Unmanned Vehicles Introduction AFOSR Overview Portfolio Overview Management Summary Technical Summary Future Direction/ Ideas Conclusions Waiting for competition results to be released.

PORTFOLIO OVERVIEW Technical Summary Introduction AFOSR Overview Portfolio Overview Management Summary Technical Summary Future Direction/ Ideas Conclusions Aerodynamic flow control and control of unsteady phenomena Lower order modeling (4) Control schemes (4) Classical, optimal Adaptive Sensor/Actuator placement (1) Controllability/Observability issues Actuators Synthetic jets, surface deflection, plasma

PORTFOLIO OVERVIEW Technical Summary Introduction AFOSR Overview Portfolio Overview Management Summary Technical Summary Future Direction/ Ideas Conclusions Active waveform control (2) Control of electromagnetic surface properties Control of deformable mirrors and beam control Initial Output Initial Output Simulation Experiment Final Output Final Output

PORTFOLIO OVERVIEW Technical Summary Introduction AFOSR Overview Portfolio Overview Management Summary Technical Summary Future Direction/ Ideas Conclusions Dynamics and Modeling System Modeling (7) Wave dynamics for engines Atomic scale processes, Quantum control theory System Identification (1) Uncertainty Characterization (1) flutter thermoacoustics F(p,q) + a2(q)pqq Acoustics Structures Combustion Fluid Dynamics Wave Speed Mistuning

PORTFOLIO OVERVIEW Technical Summary Introduction AFOSR Overview Portfolio Overview Management Summary Technical Summary Future Direction/ Ideas Conclusions General control theory Adaptive Control (5) Nonlinear Control (2) Hybrid Control (2) Other (4)

PORTFOLIO OVERVIEW Technical Summary Introduction AFOSR Overview Portfolio Overview Management Summary Technical Summary Future Direction/ Ideas Conclusions Validation & Verification (3) Toolchain Model Certificates Example control design problem: Simulink/Stateflow Control Design 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 Component Model Platform Topology Platform Mapping = Target Code For RT System Configuration Files Analysis Files RT schedules Verification Models

FY06 MURI: High Confidence Design for Distributed, Embedded Systems 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 Goal New 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 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 Frameworks and Tools for High-Confidence Design of Adaptive, Distributed Embedded Control Systems Specification, Design and Verification of Distributed Embedded Systems

Future Directions/ Ideas Introduction AFOSR Overview Portfolio Overview Management Summary Technical Summary Future Direction/ Ideas Conclusions 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)

Conclusions and Future Directions Introduction AFOSR Overview Portfolio Overview Management Summary Technical Summary Future Direction/ Ideas Conclusions 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

Backup slides

Adaptive Flight Control Transitions Some Research Highlights Adaptive Flight Control in L-JDAM Introduction AFOSR Overview Portfolio Overview Management Summary Technical Summary Some Research Highlights Future Direction/ Ideas Conclusions 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 Highlights First-ever Vision Guided Autonomous Formation Flight Introduction AFOSR Overview Portfolio Overview Management Summary Technical Summary Some Research Highlights Future Direction/ Ideas Conclusions Johnson, Georgia Tech, UCLA, MIT, VT

Some Research Highlights Vision Guided Autonomous Tracking of Ground Vehicle Beard/McLain, Brigham Young Introduction Portfolio Overview Management Summary Technical Summary Trends in Dynamics & Control Some Research Highlights Future Direction/ Ideas Conclusions

Some Research Highlights Coordinated Autonomous Tracking/ Indoor flight lab Introduction Portfolio Overview Management Summary Technical Summary Trends in Dynamics & Control Some Research Highlights Future Direction/ Ideas Conclusions How, MIT

Discovery Challenge Thrusts (DCT) Introduction AFOSR Overview Portfolio Overview Management Summary Technical Summary Future Direction/ Ideas Conclusions 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

PORTFOLIO OVERVIEW Dynamics & Controls Funding Timeline Introduction AFOSR Overview Portfolio Overview Management Summary Technical Summary Future Direction/ Ideas Conclusions Jan Jun Dec Oct Note: AFOSR BAA is continuously open. Proposals can always be submitted at any time. However, in practice there is a timeline. White Papers/ Proposal Prep Practical Deadline for Proposals, 1 Jun External Reviews Funding Decisions Beginning of Fiscal Year, 1 Oct Projected Grant Start Date, 1 Dec