Integrated Adaptive Guidance & Control for the X-37 during TAEM & A/L

1. Integrated Adaptive Guidance & Control for the X-37 duringTAEM & A/L J. Schierman Barron Associates, Inc., Charlottesville, Virginia Paul Kubiatko The Boeing Company, Huntington Beach Air Force Research Laboratory Program David Doman, PM Presented at the Aerospace Control and Guidance Systems Committee (ACGSC) Meeting Grand Island, NY Oct. 15-17

2. Presentation Outline • Motivation/program background • X-37 IAG&C program • Some details on the developed technologies • Sample experimental results • Conclusions • Boeing presentation…

3. Motivation & Technology Challenges • NASA & Air Force seeking to increase safety & reliability of next generation launch systems • House software algorithms onboard to recover the system when physically possible to: • Control effector and other subsystem failures • Larger than expected errors/dispersions • Nominal flying qualities not always recovered w/ inner-loop control reconfiguration alone • Guidanceadaptation may be necessary to account for “crippled” vehicle • For unmanned, un-powered vehicles in descent flight phases - energy management problem critical for safe landing • If vehicle characteristics have changed, energy management problem has changed • Energy managed with in-flighttrajectory command reshaping

4. Trajectory Command Generation Traj. Cmds. Guidance Laws Guidance Adaptation Algorithm Re-solve energy management problem – critical for autonomous, unpowered vehicles in gliding flight Maintain flight path stability Recover cmd. following performance to extent possible Our main focus! New approaches developed Feedback Architecture • Feedback architecture involves three main loops • Inner-loop control / Outer-loop guidance / Trajectory command generation Inner-loop Cmds. Meas. Resp. Effector Cmds. Reusable Launch Vehicle Reconfigurable Controller Vehicle Health Monitoring, Filters, Parameter ID,… Required Information Maintain attitude stability Recover cmd. following performance to extent possible We have borrowed our reconfigurable flight controls technologies We have borrowed our parameter ID technologies & developed new algorithms

5. Background - AFRL Program – ’01 to ‘04 • Air Force’s Integrated Adaptive Guidance & Control (IAG&C) flight test program • Demonstration platform: Boeing’s X-40A • Why the X-40A? Boeing accomplished 7 successful drop tests - hoped to eventually repeat drop tests w/new reconfigurable G&C algorithms • Risk reduction flight tests w/TIFS • Ensure software can run in real time • Verify simulation-based performance analysis TIFS simulated “X-40A” dynamics Flight test results presented at SAE ’04 (Colorado) Reconfigured trajectory Nominal approach trajectory Nominal touchdown aim point TIFS = Total In-Flight Simulator

6. AFRL Program Extension • IAG&C program extended – ’04-’05 • Next logical step: continue work with Boeing to develop / demonstrate IAG&C technologies for their X-37 RLV Ruddervators Speedbrake Flaperons Bodyflap

7. More technically accurate than flight tests Program Summary Chart • Description: • Demonstrate integrated adaptive guidance and control system with on-line trajectory re-targeting and reconfigurable control to compensate for control effector failures using a real-time hardware in-the -loop simulation. • Value/Benefits: • Safety and Reliability: • System can compensate for unknown model errors. • Weight: • Reduce redundancy requirements. • Key Technologies: • Adaptive / reconfigurable Guidance and Control algorithms. • Partners/Major Subcontractors • Barron Associates, Inc.

8. Program Objectives • Develop and demonstrate Integrated Adaptive Guidance and Control (IAG&C) algorithms for reusable launch vehicles by simulation analysis. • IAG&C algorithms developed under Phase II SBIRs and AFRL 6.2 X-40A IAG&C program. • Demonstrate that IAG&C architecture will automatically compensate for control effector failures and plan new feasible trajectories in real time when they exist. • Test on-line ID of ablation effects & failures • Raise technology and integration readiness levels of IAG&C system by testing algorithms in a real-time relevant simulation environment. • Utilize existing Boeing X-37 Avionics Simulation Integration Lab

9. X-37 Simulation Environments Utilized • Matlab/Simulink Environment • IAG&C System Design • Linear Analysis (phase & gain margins) • Limited Worst-on-Worst analysis capability • Shuttle Descent-Approach Program (SDAP) Environment • Simulation validation • Performance Assessment • “Worst-on-Worst” Analysis • Monte Carlo Analysis • Avionics Systems Integration Lab (ASIL) Environment • Real-Time Performance Assessment

10. Expanded Envelope – TAEM and Approach & Landing • Focus: Boeing’s X-37 drop tests • Subsonic portion of TAEM • Approach & landing • Trajectory reshaping addresses integrated TAEM/A/L mission Separation & Dive Alt = 40K ft Range = 18.8 NM Heading Alignment Cone (HAC) Nominal initial heading = -135 deg. -90o heading Acquisition w/HAC Groundtrack Alt = 22.5K ft Range = 9.5 NM 180o heading Heading Alignment Cone (HAC) Groundtrack Approach/Landing Touchdown & Rollout Alt = 10K ft Range = 4.5 NM

11. Trajectory Reshaping Approach • Need fast optimization approach - deliver new trajectory solutions in flight • Redefine complete trajectory in terms of a small number of parameters to be optimized • Once solution is obtained: map parameters back to full trajectory history • Trajectory parameters: • Initial heading angle • Altitude to start HAC turn • Altitude to start Final Flare guidance law • Dynamic pressure at touchdown • lCL, lCD: models trim CL,CD under failure condition • Optimization problem posed: • Minimize lateral maneuvering • Keeps solution from unrealistic sharp turns Drop co Groundtrack HHAC HAC Turn yrwy xrwy Defines shape of last stage of dynamic pressure profile HFF

12. Guidance & Control Laws Series of backstepping/dynamic inversion feedback loops: maps to commanded trajectory histories (V, g, X, H, etc.) that drive guidance loops Receding Horizon Optimal (RHO) Controller - - - - - Control Allocator X-37 Vehicle Longitudinal Guidance Trajectory Cmd Generation Coordinated Flight Controller Lateral Guidance Measurement Feedback… Lift, Drag Modified Sequential Least Squares (MSLS) Parameter ID

13. X-37 Drop Mission Case Study • Worst case low energy (high drag) failure - SB locked @ 65 deg. & BF locked @ 20 deg. • Ablation effects (add more drag); headwind/crosswind; navigation errors; turbulence • Simulink and RTHIL results very close • Adaptive system commands a “HAC turn” soon into the mission – “cuts the corner” to reduce downrange distance to runway – conserves energy Altitude Profile Real-Time, HIL results Ground Track Real-Time, HIL results • Adaptive system commands much steeper descent – increases kinetic energy at touchdown – allows for greater control authority to execute final flare

14. Real-Time HIL Experiment Results • 51 cases run for final set of real-time Hardware-In-the-Loop experiments • Variations included: initial heading (HAC) angle, wind direction, ablation effects, navigation errors, random turbulence, failure condition, and failure onset time All 51 cases achieved required touchdown conditions Page 2

15. Conclusions • Barron Associates focus: • Develop integrated TAEM/Approach & Landing trajectory reshaping and inner-loop reconfigurable controller • Non-real-time Matlab/Simulink experiments performed during development • Substantial number of experiments were run with dispersions in trajectory geometry, winds, failure characteristics, and other errors • Overwhelming majority of these runs resulted in safe landings • Without the advanced algorithms, failures would cause loss of vehicle • Trajectory reshaping coupled with reconfigurable inner-loop control saved vehicle from significant damage under severe effector impairments • Boeing tested algorithms in real-time simulations…