Seismic Developments at LASTI LIGOG 050184 00 Z

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Seismic Developments at LASTI LIGO-G 050184 -00 -Z Luarent Ruet Richard Mittleman Lei Zuo

Seismic Developments at LASTI LIGO-G 050184 -00 -Z Luarent Ruet Richard Mittleman Lei Zuo March 2005

 OUTLI NE 1) Geophone tilt correction HAM BSC (non-linear bending? ? ) 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

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

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

Geophone Tilt Subtraction Tilt transfer function of an inertial sensor Assumptions 1) The plant

Geophone Tilt Subtraction Tilt transfer function of an inertial sensor Assumptions 1) The plant is linear 2) The induced angle is proportional to the displacement We can then predict the tilt-induced signal from the geophones

 ( ) Fsts STS Ground ( ) K 1 Output ( )=0 Input

( ) Fsts STS Ground ( ) K 1 Output ( )=0 Input + A B ( ) + + ++ + Geo + ( ) Wit Plant FTilt K 2 FSup Feedback Control Strategy ( ) FPS + + FGeo Output + + ( ) +

BSC Transfer Functions

BSC Transfer Functions

Beam Bending

Beam Bending

Blow UP

Blow UP

vs. drive

vs. drive

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

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

BSC Stack X-Mode

BSC Stack X-Mode

Resonant Gain Results

Resonant Gain Results

Adaptive Algorithm x Plant d +- FIR-Filter y Gradient = FIR filter, of length

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

Simulink Diagram

Simulink Diagram

 ( ) Fsts STS Ground ( )=0 + Input + A B (

( ) Fsts STS Ground ( )=0 + Input + A B ( ) + ++ ( ) K 1 + PS + Geo ( ) FGeo Wit Plant ++ Adaptive Filter K 2 Feedback FSup Control Strategy ( ) FPS + + + ( ) +

HAM Optics Table Results

HAM Optics Table Results

Modal control

Modal control

Modal Control Results

Modal Control Results

Estimator Model Ground Noise (w) Drive (u) Sensor Noise (v) Plant - Gain K

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

Estimator Math Where Ce is a selector Matrix Where TFm is the model transfer

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

Noise Model

Noise Model

Estimator Results

Estimator Results

Estimator Model Ground Noise (w) Drive (u) Sensor Noise (v) Plant - Gain K

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

F dependant K

F dependant K

Results

Results

Spectrum

Spectrum

Future Estimator Work How Good does the Model Need to Be? How do we

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?

HAM Table Resonance

HAM Table Resonance

Stiffener

Stiffener

Y-Optical table

Y-Optical table

X–Position Sensors

X–Position Sensors

Noise Subtraction

Noise Subtraction

BSC Vertical Noise

BSC Vertical Noise

Vertical Noise 2

Vertical Noise 2

Horizontal Noise #1

Horizontal Noise #1

Horizontal Noise Low

Horizontal Noise Low

Horizontal Noise Mid

Horizontal Noise Mid

Horizontal Noise High

Horizontal Noise High

Transfer Function from d. Space to Position Sensors Transfer Function from d. Space to

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

THE END

THE END