orbit feedback robustness tests and system identification for facet

Jürgen Pfingstner Orbit feedback robustness and system identification for FACET

Orbit feedback robustness testsand

System identification for FACET

Jürgen Pfingstner29th of February 2012


Outline

1. Orbit feedback robustness1. Static accelerator imperfections2. Controller parameter errors3. Conclusions

2. System identification at FACET1. Principle2. Algorithms3. Results4. Conclusions


1. Orbit feedback robustness


Orbit feedback system for ML and BDS


1.1 Static accelerator imperfections


Static RF errors


Static QP strength errors


Actuator scaling errors

30% error

=> 0.5% lumi. loss


BPM scaling errors

1% error

=> 0.5% lumi. loss


1.2 Controller parameter errors


Errors in the used orbit response matrix due to ground motion (input/output directions)


Controller gain variations Δfi


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


Investigation of mode 189

SF1, SD0

SD4, SF5, SF6


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


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


2. System identification at 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


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)


2.3 Results


Excitation level vs. emittance growth


RLS no noise, 63 corr.


RLS with noise, 63 corr.


RLS only one corrector, noise


RLS more excitation with noise


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


Thank you for your attention!


LMS and SA algorithm, noise, 190 corr., Δεx=2%

orbit feedback robustness tests and system identification for facet

Documents

facetorbit feedback

different system

robustness improvement

used orbit response

sextupoles angle

facetactuator scaling

facetbpm scaling errors

small bpm readings