Barron Associates, Inc. Selected Current Research

1. Barron Associates, Inc.Selected Current Research SAE International Aerospace Control & Guidance Systems Committee Niagara Falls, NY October 14, 2008 David G. Ward (434) 973-1215 ward@barron-associates.com

2. ACGSC Meeting 102 – Grand Island, NY October 15, 2008 IAG&C for Reusable Launch Vehicles IAG&C for Ascent Working with: IAG&C for Ascent IAG&C for Re-entry Working with: IAG&C for Re-entry • Status: • High fidelity 6-DOF sim dev. (Northrop) • Reconfig. controller developed (AFRL) • Adaptive guidance matured (BAI) • Successfully recovers / reshapes trajectory to engine outs, other failures • Final Review in November • Status: • Reconfig. controller developed (BAI) • Re-entry trajectory command generation developed (BAI) • Successfully recovers / reshapes trajectory to lift & drag variations • Boeing to test robustness in high fidelity dispersion studies • Final Review in December Prof. Ping Lu • Program Objectives: • Adaptive re-entry guidance • Recover vehicle under actuator failures • Program Objectives: • Adaptive ascent guidance • Recover both 1st & 2nd stages under engine and/or actuator failures Future Access to Space Technology (FAST) Working with: Future Access to Space Technology (FAST) IAG&C for Rapid Mission Planning Working with: IAG&C for Rapid Mission Planning • Status: • Significant tool maturation • Prototype demonstrated • Lockheed to aid in • final demonstration • Work to continue in • follow-on Phase III • effort • Status: • Configuration continues to be developed (Northrop. Lockheed, Honeywell) • Aerodynamic model development continues (Northrop, Honeywell, AFRL) • ICD near completion (Northrop, Honeywell, BAI) Prof. Ping Lu Prof. Craig Kluever • Program Objectives: • Develop Mission Planning tool for RLVs • Rapid mission planning capability • Launch ready within 2 hours, 24/7 • Program Objectives: • Apply adaptive guidance technologies to FAST concept vehicle AFRL Programs / Flight Phases

3. Innovative Rotorcraft Control for Shipboard Operations NAVAIR SBIR Phase II TPOC: Mr. Dean Carico Expand operational envelope of rotorcraft from aviation capable ships • Turbulent environments • Ship motion • Rotorcraft/Ship combinations • Airwake effects • Real-time implementation & evaluation Dr. Joseph F. Horn PSU Vertical Lift Research Center of Excellence Adaptive and Learning Control Feed-forward Trim Compensation Estimate disturbances and reduce pilot workload Stochastic Disturbance Rejection Stochastic Spectral Estimation Time-varying deterministic approximation

4. Autonomous Collision Avoidance and Separation Assurance for Small UAVs in the NAS Damage Adaptation using Integrated Structural, Propulsion, and Aerodynamic Control • Novel Collision Avoidance: • Spenko, Dubowsky (MIT, 2006) • Very low computational burden • Strong safety guarantees • Robust to large uncertainties • Dynamic model-based • Phase I Objectives: • Integrate CA with BAIpath planning algorithms • Quantify processing& sensing requirements • ID HW for Ph. II demo Trajectory space formulation dramatically reduces burden On-line adaptive specs • Improved Aviation Safety: • Compensate catastrophic damage(structure, propulsion, effectors, sensors) • Approach: • On-line adaptation of subsystem design specs • Managed through smart, V&V’able middleware • Phase II Objectives: • Develop design-time tools to facilitate spec integration • Develop run-time middleware to adapt/manage specs • Demo on representative surrogate platform

5. ACGSC Meeting 102 – Grand Island, NY October 15, 2008 Advanced V&V Technologies Background Background TASS SBIR Phase III Working with: TASS SBIR Phase III • Status: • RTVV approach greatly matured • Integration into high fidelity triplex system – working w/Lockheed • Design time cert. • techniques for RTVV • investigated • Lockheed to soon • begin real-time testing • Runtime Verification & Validation (RTVV) • Monitor high risk S/W in flight (algorithm/associated code that cannot be fully certified a priori due to advanced technologies) • Shut down high risk S/W if anomalous behavior observed • Revert to simplified (standard/classical) backup mode (can be certified at design time) • Return to base/recover vehicle safely • Program Objectives: • Mature RTVV system • Integrate RTVV into triplex system with RM • Certify RTVV system at design time • Mature Flight critical neural network verification tool • Lockheed to test system in real-time simulations Challenge Problem Initiative (CPI) Working with: Challenge Problem Initiative (CPI) Mixed Critical Architecture Requirements (MCAR) Mixed Critical Architecture Requirements (MCAR) Working with: • Status: • Challenge problem selected: QF-16 (unmanned F-16 drones) autoland system certification • Focus on actual incident: incomplete mode logic resulted in hard landing during flight test • Developing MoMs, KPPs to measure cost savings of certifying autoland with new methods • RTVV application: developing • safety corridor & trajectory • prediction – is A/C currently safe? • Program Objectives: • Apply FCSSI technologies to a particular challenge problem • Barron Assoc. – focus on RTVV integration into chosen challenge problem • Status: • Developed tool to generate/organize requirements • Prototype list of requirements generated • Program Objectives: • Develop requirements for mixed critical flight systems • Focus on safety & security • Barron Assoc. – focus on RTVV integration into mixed critical architectures AFRL’s FCSSI Program: CerTA FCS, MCAR, CPI & TASS SBIRs

6. Polynomial Chaos Uncertainty Tools for Flutter • Develop methods for “non-intrusive” use of polynomial chaos • Fitting polynomial chaos representations to empirical data • Leverage domain knowledge to reduce complexity of fitting problem • Address challenges of representing uncertainty in very high order models Polynomial Chaos Fit to Eigvenvalue in Aeroelastic Model

7. Automated Updates of Tiltrotor Simulations using Experimental Data 1. automatically determine nonlinear regression structure at a particular 5. automatically update simulation data condition based upon analysis nonlinear terms (e.g., splines) = + + a + C C ... C M M M a = C ( , Mach ,...) z a Simulation 0 Simulation M i i + 2 a + - Data Tables C Data Tables ... [( 40 ) ] M a 1 2. Perform regression on data = Convert to aero table format Convert to aero table format C Experimental Flight M 4. convert to form a 1 Data Data suitable for m ± s N ( 0 , ) M M simulation data a a 1 1 table 3. compute confidence measures for the Improved fit using identified model structure parameters that will be used to update the database halfdome.arc.nasa.gov aeronautics.arc.nasa.gov NAVAIR SBIR Phase I TPOC: Mr. Sean Roark Automate simulation-updates from experimental data • Assist analyst in knowing where to update simulation and what the update should be • Structure learning • System Identification • Incremental database updates • Statistically justified and local updates Phase I Results • Data preprocessing (smoothing) • Frequency domain parameter estimation • Identify model structure for coupled, nonlinear effects • Pitch Up with Sideslip • Heave-Roll (XV-15 ground effect) • Overcome correlated actuators • Rigorous statisticalfusion of parameter estimates Simulation Update Process

8. Unmanned Underwater Riverine Craft Autonomous Operations in Riverine Environments Operations Specific mission not defined. Capabilities include: Intelligence, Surveillance, and Reconnaissance (ISR) class of operations • Persistence • Deploy/Retrieve • Identification Search, “leave behind”, etc. Riverine Environment Tidal wave and river current interactions Depth variation/stratification Confined navigation Low visibility Traffic Obstacles

9. Automated Upset Recovery System for Unmanned Air Vehicles Automated Recovery System Unusual Attitude Recovery System RL Module Inner-Loop Control OOC Arrest System Actuator Commands RL Module Guidance and Control Law Reference Out-of-Control Arrest System • robust approach for arresting large angular rates in nonlinear flight regimes Unusual Attitude Recovery System • modify commands/gains to inner-loop control to recover from early-onset upsets and unusual attitude situations Phase II objectives: • Develop upset recovery methodology • Demonstrate approach in simulations • Conduct HWIL/flight test demonstration • Develop tools to automate recovery capability A B

10. NASA SBIR/STTR Technologies Significance of Opportunity • Potential for low-cost safety improvements for commercial transport aircraft • Innovative synthetic jet actuators strategically-located on airfoil could delay stall and provide “back-up” control power • Adaptive control is required due to complex, nonlinear actuator dynamics Phase I Results • Designed and implemented adaptive control laws – verified performance analytically and in simulation • Designed wind tunnel model, novel actuators, and comprehensive Phase II test plan Phase II Work Tasks • Develop fully functional AIFAC tool(Adaptive Inverse For Actuator Compensation) • Fabricate wind tunnel models and synthetic jet actuators – optimize actuator layout • Implement real-time adaptive control system and demonstrate in closed-loop wind tunnel tests • Quantify safety improvements and develop V&V Plan to facilitate future flight tests Proposal T2.02-9831 Active Flow Control with Adaptive Design Techniques for Improved Aircraft Safety PI: Jason Burkholder / Barron Associates, Inc. – Charlottesville, VA Phase II Actuator Designs Phase II Wind Tunnel Model Design Applications • AirSTAR Testbed for AvSP/SAAP • Complex damage-adaptive FDI & control • Operation near edge of flight envelope • NASA Intelligent Flight Control System (IFCS) • Commercial and military aircraft – especially tailless “stealth” aircraft Contacts burkholder@barron-associates.com (434) 973-1215