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Seismic Developments at LASTI LIGO-G050184-00-Z

Seismic Developments at LASTI LIGO-G050184-00-Z. Luarent Ruet Richard Mittleman  Lei Zuo March 2005. OUTLI NE 1) Geophone tilt correction HAM BSC (non-linear bending??) 2) BSC Stack Characterization Resonant Gain Noise Study 3) Modal Control / Adaptive Filters 4) Estimators

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Seismic Developments at LASTI LIGO-G050184-00-Z

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  1. Seismic Developments at LASTILIGO-G050184-00-Z Luarent Ruet Richard Mittleman  Lei Zuo March 2005

  2. OUTLI NE • 1) Geophone tilt correction • HAM • BSC (non-linear bending??) • 2) BSC Stack Characterization • Resonant Gain • Noise Study • 3) Modal Control/Adaptive Filters • 4) Estimators • 5) HAM Plant Modifications • 6) System identification noise subtraction • Triple • BSC Noise Measurements • 7) Future Plans

  3. Tilt Correction The source of tilt can be divided into two categories, inherent and induced.

  4. Geophone Tilt Subtraction Tilt transfer function of an inertial sensor • Assumptions • The plant is linear • The induced angle is proportional to the displacement We can then predict the tilt-induced signal from the geophones

  5. Control Strategy K1 A B + + + + + + + + + STS () Fsts () Ground Output () FPS ()=0 PS + Input + Geo FGeo + () () Wit + () Output Plant FTilt FSup K2 Feedback

  6. BSC Transfer Functions

  7. Beam Bending

  8. Blow UP

  9. vs. drive

  10. BSC Stack Transfer Functions From the Support table to the Optics Table

  11. BSC Stack X-Mode

  12. Resonant Gain Results

  13. Adaptive Algorithm d x Plant + - e FIR-Filter y Gradient = FIR filter, of length N, has coefficients h

  14. Simulink Diagram

  15. Control Strategy K1 A B + + + + + + + + + STS () Fsts () Ground () FPS ()=0 PS + Input Geo FGeo () () () Wit + Plant Adaptive Filter + + K2 FSup Feedback

  16. HAM Optics Table Results

  17. Modal control

  18. Modal Control Results

  19. Estimator Model Ground Noise (w) Sensor Noise (v) Plant Drive (u) - Gain K + Model Modal Signals

  20. Estimator Math Where Ce is a selector Matrix Where TFm is the model transfer function if A large K will give A small K will give

  21. Noise Model

  22. Estimator Results

  23. Estimator Model Ground Noise (w) Sensor Noise (v) Plant Drive (u) - Gain Filter K + Model Modal Signals

  24. F dependant K

  25. Results

  26. Spectrum

  27. Future Estimator Work How Good does the Model Need to Be? How do we optimize the Estimator Gain vs. the Control Gain? Try it on a piece of hardware; the triple pendulum control prototype. Other Ideas?

  28. HAM Table Resonance

  29. Stiffener

  30. Y-Optical table

  31. X–Position Sensors

  32. Noise Subtraction

  33. BSC Vertical Noise

  34. Vertical Noise 2

  35. Horizontal Noise #1

  36. Horizontal Noise Low

  37. Horizontal Noise Mid

  38. Horizontal Noise High

  39. Control Equations Transfer Function from dSpace to Position Sensors Transfer Function from dSpace to Support Table Geophones Open Loop transfer function Transfer Function from Ground STS to Witness Sensor Transfer Function from dSpace to Witness Sensor Closed Loop Transfer Function from ground to witness sensor

  40. THEEND

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