orbit feedback robustness tests and system identification for facet
DESCRIPTION
Orbit feedback robustness tests and System identification for FACET. Jürgen Pfingstner 29 th of February 2012. Outline. Orbit feedback robustness Static accelerator imperfections Controller parameter errors Conclusions System identification at FACET Principle Algorithms Results - PowerPoint PPT PresentationTRANSCRIPT
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Jürgen Pfingstner Orbit feedback robustness and system identification for FACET
Orbit feedback robustness testsand
System identification for FACET
Jürgen Pfingstner29th of February 2012
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Jürgen Pfingstner Orbit feedback robustness and system identification for FACET
Outline
1. Orbit feedback robustness1. Static accelerator imperfections2. Controller parameter errors3. Conclusions
2. System identification at FACET1. Principle2. Algorithms3. Results4. Conclusions
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Jürgen Pfingstner Orbit feedback robustness and system identification for FACET
1. Orbit feedback robustness
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Jürgen Pfingstner Orbit feedback robustness and system identification for FACET
Orbit feedback system for ML and BDS
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Jürgen Pfingstner Orbit feedback robustness and system identification for FACET
1.1 Static accelerator imperfections
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Jürgen Pfingstner Orbit feedback robustness and system identification for FACET
Static RF errors
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Jürgen Pfingstner Orbit feedback robustness and system identification for FACET
Static QP strength errors
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Jürgen Pfingstner Orbit feedback robustness and system identification for FACET
Actuator scaling errors
30% error
=> 0.5% lumi. loss
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Jürgen Pfingstner Orbit feedback robustness and system identification for FACET
BPM scaling errors
1% error
=> 0.5% lumi. loss
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Jürgen Pfingstner Orbit feedback robustness and system identification for FACET
1.2 Controller parameter errors
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Jürgen Pfingstner Orbit feedback robustness and system identification for FACET
Errors in the used orbit response matrix due to ground motion (input/output directions)
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Jürgen Pfingstner Orbit feedback robustness and system identification for FACET
Controller gain variations Δfi
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Jürgen Pfingstner Orbit feedback robustness and system identification for FACET
Gain factors fi
• Smooth distribution of the fi
would be preferable for the
robustness
• Why are some patterns that
create small BPM readings
so important?
Mode 189
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Jürgen Pfingstner Orbit feedback robustness and system identification for FACET
Investigation of mode 189
SF1, SD0
SD4, SF5, SF6
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Jürgen Pfingstner Orbit feedback robustness and system identification for FACET
1.) Effect of the mode:• Luminosity loss via beam size
growth in y plane, due to a correlation x’y
• Corresponds to coupling from the x to the y plain in the FD
-> Sextupoles
2.) Possible explanation:• Setup
• General sextupole kick
• Angel y without sextupoles
• Angle with sextupoles
• Angle with sextupoles and kick in between them[-I]
S1 S2
x1 x2 -x2 x3
Uncorrected geometric aberrations
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Jürgen Pfingstner Orbit feedback robustness and system identification for FACET
Possible future work
1.) Orbit feedback:
• Robustness improvement by searching in a measured response matrix for the mode 189 and
a. Assign a high gain to itb. Correct the problem with
a different system, e.g. tuning knobs.
1.) Tuning (from discussion with Andrea, Daniel):
• Maybe a possibility to use BPM readings as
a tuning signal instead of luminosity
• Maybe also other effects at the IP can be
assigned to a BPM pattern
• Correlation studies could be interesting
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Jürgen Pfingstner Orbit feedback robustness and system identification for FACET
2. System identification at FACET
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Jürgen Pfingstner Orbit feedback robustness and system identification for FACET
2.1 Principle
Real-world system R(t)
Estimation algorithm
y(t)u(t)
... Input data (actuators)
… Output data (BPM readings)
… real-world system (accelerator)
… estimated system
• Goal:
Fit the model system in some sense to the real
system,
using u(t) and y(t)
• Ingredients
• Model assumption
• Estimation algorithm
• System excitation
Excitation
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Jürgen Pfingstner Orbit feedback robustness and system identification for FACET
2.2 Algorithms• Model:
• Task: Find R from many known measurements yk and excitations uk .
• Least squares solution: (pseudo-inverse)
• LS calculation can be modified for recursive calculation (RLS):
• Modified RLS can “forget” older values to learn time-
changing systems.
• Derivatives (easier to calculate)
- Stochastic approximation (SA)
- Least Mean Square (LMS)
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Jürgen Pfingstner Orbit feedback robustness and system identification for FACET
2.3 Results
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Jürgen Pfingstner Orbit feedback robustness and system identification for FACET
Excitation level vs. emittance growth
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Jürgen Pfingstner Orbit feedback robustness and system identification for FACET
RLS no noise, 63 corr.
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Jürgen Pfingstner Orbit feedback robustness and system identification for FACET
RLS with noise, 63 corr.
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Jürgen Pfingstner Orbit feedback robustness and system identification for FACET
RLS only one corrector, noise
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Jürgen Pfingstner Orbit feedback robustness and system identification for FACET
RLS more excitation with noise
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Jürgen Pfingstner Orbit feedback robustness and system identification for FACET
2.4 Conclusions
• Full orbit response matrix R cannot be identified in acceptable time with an parasitical excitation
• Reasons:
1) Low BPM resolution
2) Slow actuator dynamics
• Alternative scenarios
1) Identification of a subset of correctors with higher excitation.
This could be helpful to get necessary information for BBA
2) Identification of only 1 or 2 correctors for diagnostics purposes
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Jürgen Pfingstner Orbit feedback robustness and system identification for FACET
Thank you for your attention!
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Jürgen Pfingstner Orbit feedback robustness and system identification for FACET
LMS and SA algorithm, noise, 190 corr., Δεx=2%