A COMPUTER BASED AUTOROTATION TRAINER Edward Bachelder, Ph.D. Bimal L. Aponso Dongchan Lee, Ph.D. Systems Technology, Inc. Hawthorne, CA Presented at: 2005 International Helicopter Safety Symposium September 26-29, 2005, Montreal, Quebec, Canada

26-29 September IHSS, Montreal, Canada2 OVERVIEW l Motivation and concept l Technical approach l Testing and validation l Example autorotations l Computer Based Autorotation Trainer concept

26-29 September IHSS, Montreal, Canada3 MOTIVATION l For a safe outcome, helicopter autorotation requires precise and time-critical maneuvering in multiple axes. l Consequences of inappropriate timing and magnitude of control inputs can be fatal. l An autorotation trainer that could demonstrate proper control technique would be beneficial for pilot training and safety. l An autorotation trainer should allow pilots to preview and rehearse autorotations from entry conditions throughout the flight envelope.

26-29 September IHSS, Montreal, Canada4 AUTOROTATION SEQUENCE (Entry from Hover) b. c. d. a. a.) Entry b.) Stabilization c.) Maximum Flare d.) Touchdown.

26-29 September IHSS, Montreal, Canada5 THE HUMAN ELEMENT l Humans prefer to operate linear, decoupled systems to nonlinear, coupled systems  Human improvisation to unfamiliar conditions is relatively easy  Human response is: 1.More repeatable 2.Less prone to operator noise

26-29 September IHSS, Montreal, Canada6 THE HUMAN ELEMENT l Helicopter dynamics during autorotation are highly nonlinear and coupled  Nonlinear examples: 1.Lift vs rotor speed 2.Lift vs airspeed  Coupling examples: 1.Rotor speed and airspeed both affect lift 2.Collective affects rotor speed, cyclic both airspeed and rotorspeed l Scanning technique critical for coordinating proper controls sequence  During glide: Airspeed, Nr, ball, radalt  In flare: Nr, pitch, radalt

26-29 September IHSS, Montreal, Canada7 AUTOROTATION: IT’S LIKE HERDING CATS

26-29 September IHSS, Montreal, Canada8 TECHNICAL APPROACH: THE “OPTIMAL PILOT” CONCEPT l Apply optimal control theory to compute optimal trajectories and control inputs required for safe autorotation or one-engine inoperative (OEI) situations – the Optimal Pilot. l The Optimal Pilot will demonstrate autorotation trajectories over a broad range of initial and final conditions and rotorcraft configurations. l Visually integrate and display optimal inputs with the helicopter’s critical states and outside (OTW) view to provide a “sight picture.” l Preview and practice autorotations in a flight simulator using a Flight Director type display to advise the pilot of the optimal control inputs.

26-29 September IHSS, Montreal, Canada9 TECHNICAL APPROACH: OPTIMIZATION METHOD l Two-point boundary value problem – minimize objective (cost) function. l Transformation to parameter optimization problem using Direct- Collocation. l Continuous solution discretized in time using “nodes.” l Rotorcraft equations-of-motion and other non-linear constraints applied at each node. l Parameter optimization problem was solved using a commercially available Sequential Quadratic Programming (SQP) algorithm -- SNOPT l SNOPT is very well suited for near real-time generation of control commands, exhibiting stable and robust behavior for numerous entry conditions and roughly-estimated starting trajectories.

26-29 September IHSS, Montreal, Canada10 TECHNICAL APPROACH: PROBLEM FORMULATION l Cost function includes:  Sink-rate and forward speed at touchdown  Desired touchdown distance or flight time minimization (for OEI situation only) l Weightings on penalty terms were tuned to provide robust solutions across a wide range of autorotation entry conditions. l Longitudinal only, controls were collective and pitch attitude. l Constraints:  Rotorcraft equations-of-motion (represented by non-linear point-mass model).  Rotor speed overspeed and droop limits.  Pitch and collective control limits.  Maximum achievable sink rate.  Maximum pitch rate  Touchdown pitch attitude (to prevent tail strike)

26-29 September IHSS, Montreal, Canada11 TECHNICAL APPROACH: INTEGRATED DISPLAY & FLIGHT DIRECTOR

26-29 September IHSS, Montreal, Canada12 TRAINING METHOD l Compensatory tracking l Compensatory tracking with feedforward cues l Precognitive

26-29 September IHSS, Montreal, Canada13 TESTING & VALIDATION: REAL-TIME IMPLEMENTATION

26-29 September IHSS, Montreal, Canada14 TESTING & VALIDATION: FLIGHT TRAINING DEVICE l Testing performed on a fixed-base FTD by Frasca International. l Wide field-of-view visual display. l High-fidelity cockpit controls and instrument panel. l Simulated helicopter was a Bell- 206L-4. l Rotorcraft mathematical model with adequate fidelity for pilot training throughout the flight envelope including autorotation.  FAA approved under 14 CFR Parts 61 and 141.

26-29 September IHSS, Montreal, Canada15 TESTING & VALIDATION: DEVELOPMENT PROCESS l Point-mass model parameters were identified to match the flight simulation model during autorotation.  Primarily scaling of pitch and collective from optimal solution to longitudinal cyclic and collective on the simulator. l Validated using fully-coupled autorotations  A flare law was added to take over from optimal guidance during final flare and landing.  Simple lateral feedback control system was implemented to maintain heading and roll attitude.

26-29 September IHSS, Montreal, Canada16 TESTING & VALIDATION: EVALUATION METHOD l Optimizer continuously updates optimal solution based on rotorcraft states obtained from simulator. l Update is stopped when engine is failed. l Procedure:  Fly to required entry condition.  Stabilize and wait for a stable optimal solution.  Fail engine and enter automated autorotation. l Autorotation trajectory flown is based on the solution just prior to engine failure. l Safe or crash landing determined by the FTD simulation model.

26-29 September IHSS, Montreal, Canada17 TESTING & VALIDATION: EVALUATED ENTRY CONDITIONS

26-29 September IHSS, Montreal, Canada18 TESTING & VALIDATION: FULLY-COUPLED AUTOROTATIONS (400 Ft and 100 Ft Hover Entry)

26-29 September IHSS, Montreal, Canada19 TESTING & VALIDATION: CONCLUSIONS l Optimal pilot concept was validated on the Frasca FTD. l Optimal guidance allowed safe autorotation from well within the “avoid” regions of the Height- Velocity envelope. l Ability to train a pilot on autorotation technique using the flight director display was also demonstrated (results presented at AHS Forum 61, Grapevine, TX). l Incorporate Optimal Pilot concept in a CBT to allow pilots to preview autorotations.

26-29 September IHSS, Montreal, Canada20 COMPUTER BASED AUTOROTATION TRAINER: EXAMPLE AUTOROTATIONS l Autorotations flown by the optimal pilot (optimal commands are coupled to rotorcraft controls). l Show “extreme” entry conditions to illustrate the effectiveness of the concept. l Time history data: altitude (H, ft), airspeed (V, kts), pitch attitude ( , degrees), vertical velocity (w, fpm), rotor speed ( , %), collective (  c, %). l Bell 206 Model; Power failure at time = 0. l Video clips show OTW sight picture and optimal pitch attitude/collective commands.

26-29 September IHSS, Montreal, Canada21 AUTOROTATION CBT: CONTROL INPUT PREVIEW l Example cueing display for an autorotation from a 200ft hover entry l Pitch attitude preview on right, collective on left. l Tick marks show 1 second time intervals. l Pitch attitude cue indicates immediate pitch down followed by a steep pitch up with a final nose-over to avoid tail strike. l Collective cueing indicates immediate lowering of collective with collective pull at the end of the maneuver.

26-29 September IHSS, Montreal, Canada22 EXAMPLE AUTOROTATIONS: ENTRY CONDITIONS l H-V flight envelope shows “avoid” regions for Bell 206L-4. l Example autorotations shown for:  Heavy weight (4500 lbs), 400 ft hover entry (within avoid region).  Heavy weight (4500 lbs), 80 ft/60 kts entry (knee point of avoid region).  Medium weight (3600 lbs), 200 ft hover entry (within avoid region).  Medium weight (3600 lbs), 20 ft/40 kts entry (outside avoid region).

26-29 September IHSS, Montreal, Canada23 EXAMPLE AUTOROTATION TIME HISTORY (HEAVY WEIGHT, 400 FT HOVER ENTRY) (Touchdown: 18 kts, 248 fpm)

26-29 September IHSS, Montreal, Canada24 EXAMPLE AUTOROTATION VIDEO (HEAVY WEIGHT, 400 FT HOVER ENTRY) (Touchdown: 18 kts, 248 fpm)

26-29 September IHSS, Montreal, Canada25 EXAMPLE AUTOROTATION TIME HISTORY (HEAVY WEIGHT, 80 FT/60 KT ENTRY) (Touchdown: 19 kts, 221 fpm)

26-29 September IHSS, Montreal, Canada26 EXAMPLE AUTOROTATION VIDEO (HEAVY WEIGHT, 80FT/60KT ENTRY) (Touchdown: 19 kts, 221 fpm)

26-29 September IHSS, Montreal, Canada27 EXAMPLE AUTOROTATION TIME HISTORY (MEDIUM WEIGHT, 200 FT HOVER ENTRY) (Touchdown: 20 kts, 369 fpm)

26-29 September IHSS, Montreal, Canada28 EXAMPLE AUTOROTATION VIDEO (MEDIUM WEIGHT, 200 FT HOVER ENTRY) (Touchdown: 20 kts, 369 fpm)

26-29 September IHSS, Montreal, Canada29 EXAMPLE AUTOROTATION TIME HISTORY (MEDIUM WEIGHT, 20FT/40KT ENTRY) (Touchdown: 20 kts, 211 fpm)

26-29 September IHSS, Montreal, Canada30 EXAMPLE AUTOROTATION VIDEO (MEDIUM WEIGHT, 20FT/40KT ENTRY) (Touchdown: 20 kts, 211 fpm)

26-29 September IHSS, Montreal, Canada31 AUTOROTATION CBT CONCEPT l Objectives:  Provide pilots with a preview of the control inputs and trajectory required for safe autorotation from entry conditions across the flight envelope.  Provide pilots with an OTW sight picture of the autorotation.  Allow pilots to rehearse autorotations in an interactive environment. l CBT configuration:  Preset rotorcraft model parameters (for specific rotorcraft) or allow user to setup the rotorcraft model.  User sets up entry flight condition (speed, altitude, weight, wind, etc).  Allow user to adjust cost and constraint parameters (allowable rotor droop, for example)? l CBT Output:  OTW scene with or without superimposed optimal trajectory information.  Other external views to demonstrate trajectory and rotorcraft state information  Time history information

26-29 September IHSS, Montreal, Canada32 AUTOROTATION CBT: NEXT STEPS l Evaluate Industry interest and required functionality and features for:  PC based CBT (preview autorotations on the desktop).  PC based flight simulation training aid (provide cueing during flight simulation). l Refine optimal pilot algorithm:  Automatic point mass parameter estimation  Winds l Develop a graphical user interface. l Validate further using high-fidelity moving-base flight simulator.