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useful trends can be predicted by using a higher-level
structural model of the pilot. Yet, as will be shown, this
model fails to predict a pilot’s sensitivity to some key
motion parameters. All of this points to the need for
additional empirical data.
72
s
Pitch
1
(s+0.19)(s+10) 3.6
e–0.3s
s
Roll
Body-axis
pitch, roll,
and yaw
rates
Effective
torsional
pendulum
dynamics
Central nervous
system delay
Threshholds,
deg/sec
Subjective
angular
velocity
1
(s+0.16)(s+10) 2.5
e–0.3s
s
Yaw
1
1
1
(s+0.098)(s+10) 1 4.2
e–0.3s
Figure A1. Model of angular velocity sensation.
Kd
F(s)
Subjective
acceleration
and/or
orientation
to apparent
vertical
Central
nervous
system
Utricles Thresholds, ft/sec2
0.15
Gravito-inertial
force vector in
earth-fixed frame
Kd
(s+0.1)(s+1.5)
Transformation
from earth to
head frame
1
1 dmin
Figure A2. Model of specific force sensation.
73
+
+
1330 +
Desired
head
roll
angle
(s+12.5)
–
+ 1
Muscle Head
Specific torque
due to gravity
5(s+4)
(s+20)
Muscle spindle feedback
Head
roll
angle
Head
roll
inertia
specific
torque
(disturbance)
s2+10s+7.812
73.8
Figure A3. Lateral head motion control model.
Figure A4. Body-pressure sensing model.
75
Appendix B—Height Regulation
Analysis with Previous Model
To acquire insight into the potential effects of motion on
a pilot during the performance of a task, a state-of-the-art
analytical model was used. The model was applied to the
experiment described under Vertical Experiment I (sec. 4).
The task in that experiment was an altitude reposition
during hover, so it was a single-axis task. A plausible
interconnection of the relevant system dynamics in the
task is shown in figure B1. Here the pilot desires to attain
the commanded altitude, hc. Based on the motion and
visual cues, the pilot then adjusts his collective control
position dc to zero the difference between his actual and
commanded altitude.  All of the elements and connections
in figure B1 are adequately known except for the pilot
element.
hc
dc
dc h h 1 h
(s)
s2
Visual
system
Motion
system
Aircraft
Pilot
Figure B1. Altitude reposition block diagram in simulation.
In particular, what is not known is how the pilot uses
the motion and visual cues to estimate vehicle state.
Although the motion system has only acceleration as its
input, and the visual system has only position as its
input, what are each of these system’s effective outputs? It
is reasonable to assume that the motion system provides a
salient acceleration cue and that the visual system provides
a strong position cue. It is often assumed that the visual
system also supplies the velocity cue via the time-rate-ofchange
of the displayed positions. And certainly at a
steady-state velocity, the motion system provides no cue.
These very assumptions are made by Hess and Malsbury
(ref. 61) as shown in their “structural model of the pilot,”
which is reproduced in figure B2 in the context of the
altitude repositioning task discussed in section 4.
The pilot is assumed to close loops around vertical
acceleration, vertical velocity, and vertical displacement.
In the model, the acceleration is derived solely from the
motion feedback; however, it might be argued that
acceleration could also be derived from the second timederivative
of the displacement. In the acceleration feedback
path, two dynamical elements are shown. The first is a
high-pass motion filter, which attenuates motion at all
frequencies via K and at frequencies below w. Both of
these parameters are adjustable in a given simulation
facility based on the task demands and the facility’s
motion displacement capability. The second dynamic
element is the motion-system servo hardware. Using the
frequency-response testing techniques developed by
Tischler and Cauffman (ref. 46), the simulator has
vertical-axis drive dynamics (approximately) of
˙˙
˙˙ ( )
( )( )
( )( )
h
h
s
s s
sim
com
=
+ +
8 26
8 26
(B1)
　
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