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Air Force Office of Scientific Research Dynamics & Control Program Overview

Air Force Office of Scientific Research Dynamics & Control Program Overview. LtCol Scott Wells, PhD Program Manager AFOSR/ND Air Force Research Laboratory. Introduction. Introduction AFOSR Overview Portfolio Overview Management Summary Technical Summary Future Direction/ Ideas

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Air Force Office of Scientific Research Dynamics & Control Program Overview

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

  2. Introduction 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

  3. 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 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

  4. 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 AEROSPACE & MATERIALS SCIENCES Dr. Thomas Russell MATHEMATICS, INFORMATION & LIFE SCIENCES Dr. Genevieve Haddad PHYSICS AND ELECTRONICS Dr. Don Carrick ASIAN OFFICE OF AEROSPACE R&D Dr. Ken Goretta INTERNATIONAL OFFICE Dr. Mark Maurice EUROPEAN OFFICE OF AEROSPACE R & D Col. Stephen Pluntze DIRECTORATE OF POLICY AND INTERGRATION Maj Ryan Umstattd HUMAN RESOURCES Ms. Terry Hodges DIRECTOR OF CONTRACTING Ms. Trish Voss STAFF JUDGE ADVOCATE Maj. Michael Greene

  5. AFOSR Research Areas 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

  6. AFOSR Supports Basic Research Foster Revolutionary Basic Research Basic Research Funding 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 58 Technical workshops European Office of Aerospace Research and Development 205 Conferences sponsored Southern Office of Aerospace Research and Development New in May 07

  7. FY07 AF Core Basic Research InvestmentBy Discipline Introduction AFOSR Overview Portfolio Overview • Management Summary • Technical Summary Future Direction/ Ideas Conclusions

  8. PORTFOLIO OVERVIEWDynamics & 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)

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

  10. PORTFOLIO OVERVIEWDynamics & Controls Introduction AFOSR Overview Portfolio Overview • Management Summary • Technical Summary Future Direction/ Ideas Conclusions

  11. PORTFOLIO OVERVIEWTechnical 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)

  12. PORTFOLIO OVERVIEWTechnical 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

  13. PORTFOLIO OVERVIEWTechnical 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

  14. FY03 MURI: Active-Vision Control Systems forComplex 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

  15. PORTFOLIO OVERVIEWTechnical Summary Task Allocation Path Planning Target Tracking Decisions/ Computation Decentralized Centralized 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)

  16. FY01 MURI Cooperative Control of Distributed Autonomous Vehicles in Adversarial Environments • 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

  17. FY02 MURICooperative Networked Control of Dynamical Peer-to-Peer Vehicle Systems Control & Information Theory Computing & Verification Autonomous Vehicles Communications • 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 • 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

  18. FY07 MURIBehavior 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.

  19. PORTFOLIO OVERVIEWTechnical 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

  20. PORTFOLIO OVERVIEWTechnical Summary Initial Output Final Output Final Output 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 Simulation Experiment

  21. PORTFOLIO OVERVIEWTechnical 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 Wave Speed Mistuning Acoustics Structures Combustion Fluid Dynamics

  22. PORTFOLIO OVERVIEWTechnical 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)

  23. PORTFOLIO OVERVIEWTechnical 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 Фis 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 Platform Topology Component Model Platform Mapping = Target Code For RT System Configuration Files Analysis Files RT schedules Verification Models

  24. FY06 MURI: High Confidence Design forDistributed, Embedded Systems Figure 1 – Exponential Growth ofFlight-Safety-Critical Systems Is Expected due Primarily to Autonomy • 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 • 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

  25. 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)

  26. 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

  27. Backup slides

  28. 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

  29. Some Research Highlights First-ever Vision Guided Autonomous Formation Flight Johnson, Georgia Tech, UCLA, MIT, VT Introduction AFOSR Overview Portfolio Overview • Management Summary • Technical Summary Some Research Highlights Future Direction/ Ideas Conclusions

  30. 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

  31. 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

  32. 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

  33. PORTFOLIO OVERVIEWDynamics & Controls Jan Jun Oct Dec Funding Timeline Introduction AFOSR Overview Portfolio Overview • Management Summary • Technical Summary Future Direction/ Ideas Conclusions 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

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