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
OVERVIEW

Motivation and concept

Technical approach

Testing and validation

Example autorotations

Computer Based Autorotation Trainer
concept
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2005 IHSS, Montreal, Canada
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MOTIVATION




For a safe outcome, helicopter autorotation requires precise
and time-critical maneuvering in multiple axes.
Consequences of inappropriate timing and magnitude of
control inputs can be fatal.
An autorotation trainer that could demonstrate proper
control technique would be beneficial for pilot training and
safety.
An autorotation trainer should allow pilots to preview and
rehearse autorotations from entry conditions throughout the
flight envelope.
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2005 IHSS, Montreal, Canada
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AUTOROTATION SEQUENCE
(Entry from Hover)
a.) Entry
b.) Stabilization
c.) Maximum Flare
d.) Touchdown.
a.
b.
c.
d.
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2005 IHSS, Montreal, Canada
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THE HUMAN ELEMENT

Humans prefer to operate linear, decoupled systems
to nonlinear, coupled systems

Human improvisation to unfamiliar conditions is relatively
easy

Human response is:
1.
2.
More repeatable
Less prone to operator noise
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2005 IHSS, Montreal, Canada
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THE HUMAN ELEMENT

Helicopter dynamics during autorotation are highly nonlinear and
coupled

Nonlinear examples:
1.
2.

Coupling examples:
1.
2.

Lift vs rotor speed
Lift vs airspeed
Rotor speed and airspeed both affect lift
Collective affects rotor speed, cyclic both airspeed and rotorspeed
Scanning technique critical for coordinating proper controls
sequence

During glide: Airspeed, Nr, ball, radalt

In flare: Nr, pitch, radalt
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2005 IHSS, Montreal, Canada
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AUTOROTATION:
IT’S LIKE HERDING CATS
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TECHNICAL APPROACH:
THE “OPTIMAL PILOT” CONCEPT




Apply optimal control theory to compute optimal trajectories and
control inputs required for safe autorotation or one-engine
inoperative (OEI) situations – the Optimal Pilot.
The Optimal Pilot will demonstrate autorotation trajectories over a
broad range of initial and final conditions and rotorcraft
configurations.
Visually integrate and display optimal inputs with the helicopter’s
critical states and outside (OTW) view to provide a “sight picture.”
Preview and practice autorotations in a flight simulator using a Flight
Director type display to advise the pilot of the optimal control inputs.
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2005 IHSS, Montreal, Canada
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TECHNICAL APPROACH:
OPTIMIZATION METHOD






Two-point boundary value problem – minimize objective (cost)
function.
Transformation to parameter optimization problem using DirectCollocation.
Continuous solution discretized in time using “nodes.”
Rotorcraft equations-of-motion and other non-linear constraints
applied at each node.
Parameter optimization problem was solved using a commercially
available Sequential Quadratic Programming (SQP) algorithm -SNOPT
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.
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2005 IHSS, Montreal, Canada
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TECHNICAL APPROACH:
PROBLEM FORMULATION


Cost function includes:

Sink-rate and forward speed at touchdown

Desired touchdown distance or flight time minimization (for OEI situation only)
Weightings on penalty terms were tuned to provide robust
solutions across a wide range of autorotation entry conditions.

Longitudinal only, controls were collective and pitch attitude.

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)
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2005 IHSS, Montreal, Canada
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TECHNICAL APPROACH:
INTEGRATED DISPLAY & FLIGHT DIRECTOR
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TRAINING METHOD

Compensatory tracking

Compensatory tracking with feedforward cues

Precognitive
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TESTING & VALIDATION:
REAL-TIME IMPLEMENTATION
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TESTING & VALIDATION:
FLIGHT TRAINING DEVICE





Testing performed on a fixed-base
FTD by Frasca International.
Wide field-of-view visual display.
High-fidelity cockpit controls and
instrument panel.
Simulated helicopter was a Bell206L-4.
Rotorcraft mathematical model
with adequate fidelity for pilot
training throughout the flight
envelope including autorotation.

FAA approved under 14 CFR Parts 61
and 141.
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2005 IHSS, Montreal, Canada
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TESTING & VALIDATION:
DEVELOPMENT PROCESS

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.
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.
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2005 IHSS, Montreal, Canada
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TESTING & VALIDATION:
EVALUATION METHOD

Optimizer continuously updates optimal solution based on
rotorcraft states obtained from simulator.

Update is stopped when engine is failed.

Procedure:



Fly to required entry condition.

Stabilize and wait for a stable optimal solution.

Fail engine and enter automated autorotation.
Autorotation trajectory flown is based on the solution just prior to
engine failure.
Safe or crash landing determined by the FTD simulation model.
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2005 IHSS, Montreal, Canada
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TESTING & VALIDATION:
EVALUATED ENTRY CONDITIONS
600
AVOID OPERATION INSIDE
BOUNDARY LINES
550
(small scale)
Skid Height Above Surface (ft)
500
Above 4150 lbs to 4450 lbs
450
400
4150 lbs and below
350
300
Light Weight
Medium Weight
Heavy Weight
250
200
(large scale)
Skid Height Above Surface (ft)
150
100
90
100
80
70
60
50
40
30
20
10
0
4150 lbs
and below
0
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20
30
40
50
60
70
Indicated Airspeed (kts)
80
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TESTING & VALIDATION:
FULLY-COUPLED AUTOROTATIONS
(400 Ft and 100 Ft Hover Entry)
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TESTING & VALIDATION:
CONCLUSIONS




Optimal pilot concept was validated on the Frasca
FTD.
Optimal guidance allowed safe autorotation from
well within the “avoid” regions of the HeightVelocity envelope.
Ability to train a pilot on autorotation technique
using the flight director display was also
demonstrated (results presented at AHS Forum
61, Grapevine, TX).
Incorporate Optimal Pilot concept in a CBT to
allow pilots to preview autorotations.
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2005 IHSS, Montreal, Canada
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COMPUTER BASED AUTOROTATION TRAINER:
EXAMPLE AUTOROTATIONS





Autorotations flown by the optimal pilot (optimal commands
are coupled to rotorcraft controls).
Show “extreme” entry conditions to illustrate the
effectiveness of the concept.
Time history data: altitude (H, ft), airspeed (V, kts), pitch
attitude (, degrees), vertical velocity (w, fpm), rotor speed
(, %), collective (c, %).
Bell 206 Model; Power failure at time = 0.
Video clips show OTW sight picture and optimal pitch
attitude/collective commands.
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2005 IHSS, Montreal, Canada
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AUTOROTATION CBT:
CONTROL INPUT PREVIEW





Example cueing display for an
autorotation from a 200ft hover
entry
Pitch attitude preview on right,
collective on left.
Tick marks show 1 second time
intervals.
Pitch attitude cue indicates
immediate pitch down followed
by a steep pitch up with a final
nose-over to avoid tail strike.
Collective cueing indicates
immediate lowering of collective
with collective pull at the end of
the maneuver.
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EXAMPLE AUTOROTATIONS:
ENTRY CONDITIONS
600
500



Heavy weight (4500 lbs), 400 ft
hover entry (within avoid region).
(small
Example autorotations shown for:

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).
AVOID OPERATION INSIDE
BOUNDARY
LINES
550
Skid Height scale)
Above Surface (ft)

H-V flight envelope shows “avoid”
regions for Bell 206L-4.
Above 4150 lbs to 4450
lbs
450
400
4150 lbs and
below
350
300
Medium Weight
250
Heavy Weight
200
150
(large
scale) Surface (ft)
Skid Height Above

100
90
100
80
70
60
50
40
30
20
10
0
0
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2005 IHSS, Montreal, Canada
10
20
30
40
50
60
70
Indicated Airspeed (kts)
80
4150
lbs
and
below
22
130
EXAMPLE AUTOROTATION TIME HISTORY
(HEAVY WEIGHT, 400 FT HOVER ENTRY)
(Touchdown: 18 kts, 248 fpm)
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EXAMPLE AUTOROTATION VIDEO
(HEAVY WEIGHT, 400 FT HOVER ENTRY)
(Touchdown: 18 kts, 248 fpm)
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EXAMPLE AUTOROTATION TIME HISTORY
(HEAVY WEIGHT, 80 FT/60 KT ENTRY)
(Touchdown: 19 kts, 221 fpm)
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EXAMPLE AUTOROTATION VIDEO
(HEAVY WEIGHT, 80FT/60KT ENTRY)
(Touchdown: 19 kts, 221 fpm)
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EXAMPLE AUTOROTATION TIME HISTORY
(MEDIUM WEIGHT, 200 FT HOVER ENTRY)
(Touchdown: 20 kts, 369 fpm)
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EXAMPLE AUTOROTATION VIDEO
(MEDIUM WEIGHT, 200 FT HOVER ENTRY)
(Touchdown: 20 kts, 369 fpm)
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EXAMPLE AUTOROTATION TIME HISTORY
(MEDIUM WEIGHT, 20FT/40KT ENTRY)
(Touchdown: 20 kts, 211 fpm)
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EXAMPLE AUTOROTATION VIDEO
(MEDIUM WEIGHT, 20FT/40KT ENTRY)
(Touchdown: 20 kts, 211 fpm)
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2005 IHSS, Montreal, Canada
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AUTOROTATION CBT CONCEPT



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.
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)?
CBT Output:

OTW scene with or without superimposed optimal trajectory information.

Other external views to demonstrate trajectory and rotorcraft state information

Time history information
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2005 IHSS, Montreal, Canada
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AUTOROTATION CBT:
NEXT STEPS




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).
Refine optimal pilot algorithm:

Automatic point mass parameter estimation

Winds
Develop a graphical user interface.
Validate further using high-fidelity moving-base flight
simulator.
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2005 IHSS, Montreal, Canada
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