1410 Sachem Place ◊ Suite 202 ◊ Charlottesville, VA Integrated Adaptive Guidance & Control for the X-37 during TAEM & 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

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

3 ACGSC, October, 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 Guidance adaptation 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-flight trajectory command reshaping

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

5 ACGSC, October, 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 Nominal approach trajectory Reconfigured trajectory Nominal touchdown aim point TIFS = Total In-Flight Simulator TIFS simulated “X-40A” dynamics Flight test results presented at SAE ’04 (Colorado)

6 ACGSC, October, 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 Bodyflap Flaperons

Engineering, Operations & Technology | Phantom Works Copyright © 2008 The Boeing Company. All rights reserved. Distribution A, Cleared for Public Release, Distribution Unlimited. Case No. 88ABW WPAFB, OH 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. More technically accurate than flight tests

Engineering, Operations & Technology | Phantom Works Copyright © 2008 The Boeing Company. All rights reserved. Distribution A, Cleared for Public Release, Distribution Unlimited. Case No. 88ABW WPAFB, OH 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

Engineering, Operations & Technology | Phantom Works Copyright © 2008 The Boeing Company. All rights reserved. Distribution A, Cleared for Public Release, Distribution Unlimited. Case No. 88ABW WPAFB, OH 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 ACGSC, October, 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 Groundtrack Heading Alignment Cone (HAC) Approach/Landing Separation & Dive Touchdown & Rollout Alt = 40K ft Range = 18.8 NM Alt = 22.5K ft Range = 9.5 NM Alt = 10K ft Range = 4.5 NM Acquisition w/HAC Groundtrack Nominal initial heading = -135 deg. Heading Alignment Cone (HAC) 180 o heading -90 o heading

11 ACGSC, October, 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 CL, CD : models trim C L,C D under failure condition Optimization problem posed: Minimize lateral maneuvering Keeps solution from unrealistic sharp turns Groundtrack y rwy x rwy oo H HAC Drop HAC Turn H FF Defines shape of last stage of dynamic pressure profile

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

13 ACGSC, October, X-37 Drop Mission Case Study Worst case low energy (high drag) failure - SB 65 deg. & BF 20 deg. Ablation effects (add more drag); headwind/crosswind; navigation errors; turbulence Altitude Profile Ground Track 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 Adaptive system commands much steeper descent – increases kinetic energy at touchdown – allows for greater control authority to execute final flare Real-Time, HIL results

14 ACGSC, October, 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 Page 2 All 51 cases achieved required touchdown conditions

15 ACGSC, October, 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…