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Increasing computing performance of ADCS subsystems in small satellites for earth observation
Johan Carvajal-Godínez, Morteza Haghayegh, Allan Granados, Jaan Viru and Jian Guo
Space Engineering Department Faculty of Aerospace Engineering
Delft University of Technology
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Outline • Introduction • ADCS challenges • “Y model” approach for ADCS • DelFFi ADCS modeling and simulation • OBC Hardware selection • ADCS software integration • Conclusions
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What does Earth Observation and Formation Flying have in common?
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Challenging ADCS subsystem
ü Multiple attitude modes ü High pointing accuracy ü Precise three axis control algorithms ü High resolution data types from sensors ü Onboard sensor calibration ü Fault detection and correction ü onboard functions for autonomous
operation More complex software à improving OBC capabilities for ADCS
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DelFFi Mission Statement
Source: SSE TU Delft
“The DelFFi mission shall demonstrate autonomous formation flying and provide enhanced scientific return within QB50 from 2016 onwards, by utilizing two identical triple-unit Cubesats of TU Delft which further advance the Delfi-n3Xt platform.”
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DelFFi ADCS software development
“Y approach”
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ADCS Model
OBC platform
ADCS SW Integration
DelFFi ADCS software development
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DelFFi ADCS simulation model ADCS Model
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DelFFi ADCS input requirements
VP: Velocity pointing
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ADCS software architecture (initial)
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DelFFi ADCS simulation model
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ADCS model profiling Goal: Identifying the most demanding blocks inside the ADCS model by measuring relative CPU time utilization during simulation, for later code acceleration with digital signal processor (DSP) Process Steps: • Implement ADCS simulation model in Simulink • Setup up the Matlab profiler to collect model performance
data • Setup up the simulation environment for ADCS model • Run the model profiler • Analyze ADCS model performance data • Select most demanding model block for code acceleration
with DSP
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Simulation environment setup
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ADCS model profiling results
Other functions include: • Environment initialization • ADCS Mode determination algorithm • Other attitude modes (Safe and De-Tumbling) • Simulation Termination
Block Function Number of calls
Percentage of relative CPU time usage during simulation
Attitude estimation (EKF) 110 000 52% Velocity Pointing mode 110 000 14% Other OBC functions 110 000 34%
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DelFFi OBC selection OBC
platform
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OBC architecture trade off
Better computing
performance
Multicore CPU
Heterogeneous SoC
Beagleboard XM
OBC Requirements: • Code acceleration support • Build system support from
open embedded community • I2C, SPI and UART support • Power efficient floating point
unit performance w.r.t. FPGA • COTS available • Open hardware and software
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Hardware Test bed Beagleboard XM: - COTS and open
hardware/software platform
- TI DM3730 SoC (ARM processor +Digital Signal Processor)
- Build system support from embedded community (Yocto project)
- Continuous integration support (Jenkins)
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ADCS software build system
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Results on services and drivers support
Operating system size with all driver support is 18 MB
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DelFFi ADCS software development
ADCS SW Integration
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DelFFi ADCS architecture (final)
Accelerated block Non Accelerated blocks
ADCS application
ADCS OBC
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Communication inside ADCS application
Optimized buffer size for EKF requirements
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Integration Results • EKF execution speedup of 5-10 times (based
on baseline performance for FFT)
• ADCS software footprint size is less than 20 MB (regular footprint is 100-200 MB)
• Memory size for data exchange between ARM-DSP was reduced to 64MB ( initially 128 MB)
• Fully automated operating system image generation with Yocto project + Jenkins
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Conclusions • “Y approach ” was introduced and
implemented for DelFFi ADCS software architecture exploration
• Model profiling technique helped to identify and quantify computing demand for ADCS attitude estimation algorithm.
• Using a build system (Yocto project) and continuous integration tools improved software productivity problem
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Further Work • Continue the work in the porting of Simulink
model to BeagleBoard XM board
• Compare ADCS performance with results from Flight Model OBC (Benchmark)
• Continue to investigate on code acceleration in space software applications with heterogeneous onboard processors