Transcript PFI_physical_modelling_overview-v2.ppt

PFI physical modeling overview
A simulation of the PFI bench,
metrology camera and PFICS control
loop
Peter Mao, Caltech
Nov 2012
Purpose
Develop all PFICS algorithms before hardware
delivery. These algorithms include:
•Fiducial fiber identification
•Science fiber identification
•Control loop for Cobra convergence
These algorithms are being tested in the presence of
physical errors such as:
•Measurement errors (due to seeing, distortion,
calibration, etc.)
•Setup imperfections such as broken fibers, faulty
connectors, and missing rails.
Fiducial fiber identification
• Model is in an advanced
state.
• Algorithm works with up to
40% of the fibers
missing/broken.
• All fiducial fibers (both
working and broken) are
identified.
• The position angle (PA) of
the bench is determined to
<10urad (<2um for edge
fibers)
• This step is highly reliable
and NOT computationally
intensive.
Science Fiber identification
• We have demonstrated (though not shown
here) SF identification at the >90% level from
a blind start
• This identification can proceed with a
relatively poor reading on the center of the
distortion pattern
• This problem is expected to be fairly easy, so
efforts were redirected towards other
problems
Control loop for cobra convergence
An effective control loop for Cobra convergence requires:
•finding the distortion center (surprisingly difficult)
•modeling the distortion (work is only beginning)
•modeling the Cobra response (needs better data)
•modeling the measurement error (needs better data)
Distortion center location
• Using only the fiducial fibers, we
can locate the center of a
SYMMETRIC distortion pattern to
100 microns.
• 100 micron error in the distortion
center results in 10 microns of
error in the position
reconstruction.
• INCOMPLETE: looking for a way
to determine the DC to better
than 10 micron. This may be
limited by the measurement
error.
PFI/Cobra response and
convergence
• Using current Cobra motion data,
we can model Cobra
convergence.
• This allows us to predict the
consequences of any particular
error budget allocation.
• Model shows that measurement
error adds significantly to the
number of iterations required for
convergence.
• Many refinements needed for the
model
• it takes 10 iterations to
get 95% of the fibers on
target
• it takes 12 iterations to
get 99% on target
Future work
• Complete the model for science fiber
identification.
• Incorporate data from new Cobra measurements.
• Incorporate data from new metrology camera
measurements.
• Develop a reliable and accurate method for
determining the location and orientation of the
distortion pattern.
• Model the error contribution of the MC to PFI
coordinate transform.