Bernard, William

Geohagan, Kevin

Leggett, Jared

Oliver, T. Emerson

Strickland, Dennis J.

NASA Technical Reports Server

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SPACE LAUNCH SYSTEM
National Aeronautics and Space Administration
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5 . . . 4 . . . 3 . . . 2 . . . 1 . . .
AAS 18-132: 6DOF Testing of the SLS 
Inertial Navigation Unit Kevin Geohagan, MSFC/ESSCA
William Bernard, MSFC/ESSCA
T. Emerson Oliver, MSFC/ESSCA
Jared Leggett, MSFC/NASA
Dennis J. Strickland, MSFC/ESSCA
.1
https://ntrs.nasa.gov/search.jsp?R=20180002050 2019-08-30T12:50:28+00:00Z
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Test Background/Objectives
.2
• Test proposed & conducted to:
– Gain insight into gyrocompassing performance of a flight-like 
RINU under representative SLS on-pad dynamics
– Provide gyrocompassing test data for validation of the RINU 
performance model
– Test planned pre-launch RINU operational procedures
– Assess the robustness of the RINU GCA algorithm to larger-than-
predicted SLS on-pad dynamic environments
• Performed in MSFC 6DOF Table Facility—
formerly Contact Dynamics Simulation Lab 
(CDSL), site of:
– Hubble Space Telescope deployment, 
service, and Flight Support System 
(for deorbit), docking/berthing
– Shuttle/ISS docking/berthing
– HWIL Space Shuttle Arm training
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Facility Test Equipment/Test Article
.3
– Theodolite, North-referenced 
mirrors
• measures RINU true azimuth
– Leica Laser Tracker System (LLTS)
• tracks position and
attitude of table
– Leica inclinometer
• co-located with
RINU to measure tilt
• Test Article is RINU Flight-
Equivalent Unit (FEU)
– identical hardware to RINU 
flight units
– “equivalent” because 
acceptance testing is 
abbreviated
• no shock/vibration/thermal 
testing
• Equipment:
– 6DOF table with ~4m2 top
• Stewart platform (hexapod) design
• hydraulically actuated
– *ARTEMIS HWIL simulation 
framework
• commands table dynamics
• emulates SLS flight software
– *MAESTRO user interface
• live data display
• provides test operator interface
• records1553 bus traffic
– GPS antenna for accurate time-
tagging of data
– Cameras, displays
– Power supply, power quality 
monitoring/recording system
* Used for SLS-Program-requirement-verification HWIL testing
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Test Operational Flow
.4
• Power on ARTEMIS/MAESTRO (HWIL software), table hydraulics & 
control, data recording/monitoring devices
– confirm nominal operation
• Power on RINU, allow to thermally stabilize
• Initialize RINU
• Initiate 6DOF table dynamics
• Command RINU to GCA mode, gyrocompass for 60 minutes
• Command RINU to navigation mode
• Table dynamics end; lower table and power off
• Measure RINU azimuth via theodolite
• Power off RINU
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Table Motion
.5
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Test Case Summary
.6
Purpose Description
Preliminary Testing Static GCA only; no nav
Baseline GCA Static GCA with nav
Twist & Sway
3 dynamic twist & sway models:
• Latest SLS
• Early SLS
• Vendor heritage
Robustness Testing
SLS twist & sway with scaled up 
dynamics
24-Hour Static 24-hour static GCA
7-Hour GCA 7-hour dynamic GCA
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Post-Test Analysis: Sensor Bypass
.7
• Purpose:
– To provide validation evidence for RINU model by comparing 
hardware/model  performance
• Procedure:
– delta-V & delta-ϴ
inputs to RINU GCA
algorithm reported on
1553
– input to the RINU 
performance model’s
GCA code (bypassing
sensor model)
– compare GCA solution
to hardware
Error growth
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Post-Test Analysis: Sensor Bypass
.8
• Analysis of frame counter
shows some missing data
– due to asynchronous
polling effects
• Missing data corresponds
with some anomalous
error growth times
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Post-Test Analysis: Sensor Bypass
.9
• Missing data was replaced
with interpolated values
• Using interpolated data,
comparison results were
improved
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Post-Test Analysis: Sensor Bypass
.10
Twist & Sway Dynamics
Difference in GCA Azimuth,
radians
Early SLS
-0.000123
0.000162
Vendor Heritage
0.000128
0.000048
Latest SLS -0.000054
SLS X4 0.000026
SLS X8 -0.000078
SLS X16 -0.000199
SLS X32 -0.000316
SLS X64 -0.000339
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Post-Test Analysis: Monte Carlo Comparison
.11
• Purpose:
– Assess hardware test performance relative to expectation
• Procedure:
– 500-case Monte Carlos
• Same twist & sway dynamics used to produce table dynamics
• 2 error budgets:
–vendor capability estimate (labeled “NEB”)
–derived from ATP test limits (labeled “ATP”)
– Azimuth error for Monte Carlo solutions co-plotted against that 
measured in test
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Post-Test Analysis: Monte Carlo Comparison
.12
• Vendor heritage case very near bounds of model prediction
– Possible explanations:
• dynamics not structurally derived
• large-amplitude dynamics—possibly stressing table control
Measured error 
very near bounds
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.13
• All scaled-dynamics cases comfortably within modeled bounds
• Negligible sensitivity to error budget across all tested twist & 
sway environments
Post-Test Analysis: Monte Carlo Comparison
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Post-Test Analysis: Sensor Noise Characterization
.14
• Purpose:
– Examine RINU sensor noise and error characteristics
– Provide validation evidence for RINU performance model
• Procedure:
– Data from 24-hour runs used to perform Allan Deviation, spectral 
analysis
– Recreated test condition using RINU model for comparison
• Findings to feed back to change recommendations for RINU 
model developers
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Conclusions
.15
• Testing achieved all test objectives
– Gained insight into GCA performance
– Produced test data for RINU model validation
– Tested pre-launch RINU operational procedures
– Assessed RINU GCA robustness
• Post-test analysis providing RINU model validation insight
– Sensor bypass analysis provided direct GCA solution comparison
– Modeled sensor noise/error characteristics were directly assessed 
via Allan Deviation and spectral analysis
• Will likely drive future model updates
• RINU hardware GCA performance was within expectation for all 
SLS and SLS-derived (scaled) environments
– Some potential lack of conservatism in modeled performance 
under vendor heritage environment
• May merit further testing to confirm
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.16
Thank you!


6DOF Testing of the SLS Inertial Navigation Unit

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