Anzalone, Evan

Geohagan, Kevin

Oliver, T. Emerson

Park, Thomas

NASA Technical Reports Server

www.nasa.gov/sls
SPACE LAUNCH SYSTEM
National Aeronautics and Space Administration
www.nasa.gov/sls
5 . . . 4 . . . 3 . . . 2 . . . 1 . . .
SLS Model Based Design: 
A Navigation Perspective
T. Emerson Oliver – ted.e.oliver@nasa.gov
Evan Anzalone, Thomas Park, 
Kevin Geohagan
2018 AAS GNC Conference
.1
https://ntrs.nasa.gov/search.jsp?R=20180002045 2019-08-30T12:49:06+00:00Z
www.nasa.gov/sls
Overview
.2
• Introduction to Space Launch Systems (SLS)
• SLS Requirements and Design Math Models (DMMS)
• The SLS GN&C Model 
• The SLS Inertial Navigation System (INS) Performance 
Model
• Marshall Advanced GPS Model for Analysis (MAGMA)
• Conclusions and Lessons Learned
www.nasa.gov/sls
Introduction to Space Launch System (SLS)
.3
EM-1 EM-2
EM-3
• NASA is developing a phased plan to deep space exploration enabled by SLS, an 
evolution of Launch vehicles.
– Currently completing the design and building the Block 1 vehicle
– In the process of Block 1B design
www.nasa.gov/sls
SLS SE&I Model Based Design
.4
• Reduced Program structure
• Emphasis on heritage hardware
• Relatively sparse requirements set 
over previous design projects
• DMMs convey the design
– Controlled at program level
– Maturity/limitations/use tightly 
tracked
– Component models are verified 
against vendor design and validated 
against flight hardware (or equiv.)
– Physics models (e.g. 6DOF sim) 
verified against other simulations and 
validated with test data.
– Model parameters of high sensitivity 
can be elevated to requirements
• SLS Navigation Supports Level II and 
Level III
• Example
– Level II DMMs: GN&C Model, MAVERIC 
(6DOF Sim)
– Level III DMMs: INS Performance, GPS
Level I
SLS Program Structure
Requirements Interface
Requirements Interface
“Build a vehicle.”
► INS I/F, accuracy
► GPS I/F, accuracy
► TVC performance
Level II
Integrated Vehicle
Level III
Vehicle Elements
Element &
Component
Models
Integrated
Models
Certification
GN&C
Int. Loads & Dyn.
Int. Avionics (FSW, SIL)
Core Stage
Upper Stage
Engine
► Payload performance
► Target accuracy
► Payload impact
N
av
ig
at
io
n 
Te
am
www.nasa.gov/sls
GN&C Model
.5
• Began as pilot program 2010
• Common GN&C code across SLS 
Disciplines & Functions
• Efficient GNC/FSW Process
• DMM Contents
– Executable Algorithms
– Parameter Definition
– Technical Memorandum
– Interface assumptions
– Unit test cases
• GN&C/Navigation Model
– Inertial Measurement Processing
– State derived quantities
– RINU Initialization
– RINU FDIR Parameters
– GCA Convergence check
– RINU Frame check
– GPS Measurement Processing
– SDINS algorithms
– Navigation EKF (Block 1B only)
www.nasa.gov/sls
INS Performance Model
.6
• RINU: Redundant Inertial Navigation Unit
• Level II Requirements Definition
– Interface and frequency response
– Performance constrained with reference 
trajectory
– Reduction in requirements with explicit 
modeling
• Level III Model Description
– Detailed instrument error modeling
– Algorithms which affect performance
– Detailed interface model
• Verified against vendor documentation, 
FQT data, and analysis
• Validated against test data
– GCA 6DOF Test 
– Frequency response test
– Vendor ATP/QTP data
• Analyses Performed
– Navigation performance
– Gyrocompassing alignment
– Coning/Sculling 
– Integration into vehicle 6DOF
www.nasa.gov/sls
MAGMA GPS Model
.7
• Marshall Advanced GPS Model 
for Analysis
• Framework developed to support
– Requirements development, 
– Early Navigation System design 
– Seed Level III DMM development
• Level II Requirements
– Interface definition
– Measurement accuracy
• Functional Components
– Detailed truth model
– SV and Receiver Antenna 
modeling
– Receiver hardware modeling
– Receiver software modeling
• Models measurement availability, 
accuracy, and latency
Simulation of GPS availability within 2D plane
www.nasa.gov/sls
Conclusion
.8
• Implementation of MBD on SLS has significantly increased efficiency
– Reduced requirements burden
– Provide explicit communication of component and integrated system design
• Provides a mechanism for 
– Detailed modeling and design insight
– Identification of key vehicle sensitivities
– Gaining additional insight through testing and validation process
– Enforcing rigor in modeling through validation
• DMM V&V process forces high fidelity emulation of hardware 
• Lessons Learned:
– Model form and function should consider user and developer
– GN&C Model
• Software requirements drive the software test program
• Approach conflicted with established FSW processes and culture
– Component models, 
• Good data requirements and supplier integration are key to enabling process
• V&V plans should be defined early to support data requirements definition and to identify 
gaps which require additional testing.
• Sensitivity analyses should be used to identify key performance drivers
• Commonality between HWIL models and Performance/Analysis models reduces cross-
validation effort in verification
www.nasa.gov/sls
.9
Thank you!
Any questions?


SLS Model Based Design: A Navigation Perspective

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