it follows that

1
It follows that ) ˆ ( e e and ˆ ˆ e e ˆ Step 1: Assume system to be described as , where y is the output, u is the input and is the vector of all unknown parameters. ) , , ( u y f y ) ˆ , , ˆ ( ˆ u y f y ˆ Step 2: A mathematical model with the same form, with different parameter values is used as a learning model such that y y e ˆ Step 3: The output error vector, e , is defined as . Step 4: Manipulate such that the output is equal to zero. Step 5: 1. Shubham K. Bhat, T.P.Kurzweg, Allon Guez "Learning Identification of Opto-Electronic Automation Systems", IEEE Journal of Special Topics in Quantum Electronics, May/June 2006. 2. Shubham K. Bhat, T.P.Kurzweg, Allon Guez,” Simulation and Experimental Verification of Model Based Opto-Electronic Packaging Automation”, International Conference on Optics and Opto-electronics Conference, Dehradun, India, December 12-15, 2005. 3. Shubham K. Bhat, T.P.Kurzweg, Allon Guez, “Advanced Packaging Automation for Opto-Electronic Systems”, IEEE Lightwave Conference, New York, October 2004 . 4. T.P. Kurzweg, A. Guez, S.K. Bhat, "Model Based Opto-Electronic Packaging Automation," IEEE Journal of Special Topics in Quantum Electronics, Vol.10, No. 3, May/June 2004, pp.445-454. m e S ˆ 1 ( m is unknown and K is known ) We present the above optical system with one unknown( slit width -“a”) exhibiting input-output differential equation Ku y m y The variables u, y, and are to be measured y Step 1: u K x m x 0 0 1 0 1 x y and 2 x y { } Step 2: u K x m x ˆ 0 ˆ ˆ 0 1 0 ˆ { Assume estimated model and } x x e ˆ The Sensitivity coefficients are contained in Step 3: where , ˆ ˆ T y y y y e m x m e ˆ ˆ ˆ Automation is the key to high volume, low cost, and high consistency manufacturing ensuring performance, reliability, and quality. No standard for OE packaging and assembly automation. Packaging is critical to success or failure of optical microsystems. 60-80 % cost of optical component/system is in packaging. Manual Semi-Automatic Fiber-Fiber Alignment Optical setup Amplifiers, Encoders Interpolators, Motion Controller Visit www.ece.drexel.edu/opticslab/results/Automation.wmv to watch the Model Based Control Vi Visit www.ece.drexel.edu/opticslab/results/learning.wmv to watch the Learning Identification Vid Power Meter readings Comparison of Power Levels 1)Current State-of-the-Art = 0.644 uW 2)Our Technique: Model Based Control = 1.55 uW 3)Our Technique: Learning Identification Control = 1.675 uW Real System Adjustment Scheme Model + - ) ( ˆ t y ) ( t u ) ( t y ) ( t e Input Output Error Estimated model Inaccurate modeling could lead to deviation from the actual values. Activated at a lower sampling frequency. Specific and appropriate tasks. Provides opportunities for the system to improve upon its power model. Adjust the accuracy on the basis of “experienced evidence.” )) ( sin(tan 2 ( cos ) ) ) ( (tan sin 2 ( sin ) ( 1 2 1 2 z x ka z x kb c A x I = wavelength = 630 nm k = wave number associated with the wavelength a = center-to-center separation = 32 um b = width of the slit = 18 um z = distance of propagation =1000 um For the double slit aperture, the irradiance at any point in space is given as: Criteria for choosing If is too large, the schemes will diverge. m ˆ If is too small, then will approach m very slowly. Selection of a suitable determined by a trial and error process. The initial feed-forward set point is obtained from the optical power modeling done in MATLAB. This set point is given as an input to the PMDI motion control software which follows a PID loop by measuring the power. We start with an initial guessed value of “a” which is the center-to- center separation between the slits. The inner PID loop is repeated 5 times after which the outer learning loop comes into effect. The learning loop updates the estimated set points and tracks the actual set point. In this simulation, the learning algorithm was run 28 times. We track “m” to be 1.86 which relates to a slit width “a” of 32um. e S m T ˆ Learning Equation : Initial X Encoder Position = 10477 Final X Encoder Position = 10810 Initial Y Encoder position = 24 Final Y Encoder position = 25 Thus, we show an increase in power level reached along with increased efficiency and accuracy.

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Real System. Input. Output. +. Adjustment Scheme. -. Error. Estimated model. Model. Step 1: Assume system to be described as , where y is the output, u is the input and is the vector of all unknown parameters. - PowerPoint PPT Presentation

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Page 1: It follows that

It follows that )ˆ(ee and

ˆˆ

ee

Step 1: Assume system to be described as , where y is the output, u is the input and is the vector of all unknown parameters.

),,( uyfy

)ˆ,,ˆ(ˆ uyfy Step 2: A mathematical model with the same form, with different parameter values is used as a learning model such that

yye ˆStep 3: The output error vector, e , is defined as .

Step 4: Manipulate such that the output is equal to zero.

Step 5:

1. Shubham K. Bhat, T.P.Kurzweg, Allon Guez "Learning Identification of Opto-Electronic Automation Systems", IEEE Journal of Special Topics in Quantum Electronics, May/June 2006.

2. Shubham K. Bhat, T.P.Kurzweg, Allon Guez,” Simulation and Experimental Verification of Model Based Opto-Electronic Packaging Automation”, International Conference on Optics and Opto-electronics Conference, Dehradun, India, December 12-15, 2005.

3. Shubham K. Bhat, T.P.Kurzweg, Allon Guez, “Advanced Packaging Automation for Opto-Electronic Systems”, IEEE Lightwave Conference, New York, October 2004 .

4. T.P. Kurzweg, A. Guez, S.K. Bhat, "Model Based Opto-Electronic Packaging Automation," IEEE Journal of Special Topics in Quantum Electronics, Vol.10, No. 3, May/June 2004, pp.445-454.

m

eS

ˆ1

( m is unknown and K is known )

We present the above optical system with one unknown( slit width -“a”) exhibiting input-output differential equation Kuymy

The variables u, y, and are to be measuredy

Step 1: uK

xm

x

0

0

10 1xy and 2xy { }

Step 2: uK

xm

x ˆ0

ˆˆ0

10ˆ

{ Assume estimated model and }xxe ˆ

The Sensitivity coefficients are contained in

Step 3: where ,ˆˆT

yyyye

m

x

m

ˆ

ˆ

Automation is the key to high volume, low cost, and high consistency manufacturing ensuring performance, reliability, and quality.

No standard for OE packaging and assembly automation.

Packaging is critical to success or failure of optical microsystems.

60-80 % cost of optical component/system is in packaging.

Manual Semi-AutomaticFiber-Fiber Alignment

Optical setup Amplifiers, Encoders Interpolators, Motion Controller

Visit www.ece.drexel.edu/opticslab/results/Automation.wmv to watch the Model Based Control VideoVisit www.ece.drexel.edu/opticslab/results/learning.wmv to watch the Learning Identification Video

Power Meter readings

Comparison of Power Levels

1) Current State-of-the-Art = 0.644 uW

2) Our Technique: Model Based Control = 1.55 uW

3) Our Technique: Learning Identification Control = 1.675 uW

Real SystemReal System

Adjustment Scheme

Adjustment Scheme

ModelModel

+

- )(ˆ ty

)(tu )(ty

)(te

Input Output

ErrorEstimatedmodel

Inaccurate modeling could lead to deviation from the actual values.

Activated at a lower sampling frequency.

Specific and appropriate tasks.

Provides opportunities for the system to improve upon its power model.

Adjust the accuracy on the basis of “experienced evidence.”

)))(sin(tan2

(cos)))((tansin2

(sin)( 1212

z

xka

z

xkbcAxI

= wavelength = 630 nmk = wave number associated with the wavelength a = center-to-center separation = 32 umb = width of the slit = 18 umz = distance of propagation =1000 um

For the double slit aperture, the irradiance at any point in space is given as:

Criteria for choosing

If is too large, the schemes will diverge.

mIf is too small, then will approach m

very slowly.

Selection of a suitable determined by a trial and error process.

•The initial feed-forward set point is obtained from the optical power modeling done in MATLAB.

•This set point is given as an input to the PMDI motion control software which follows a PID loop by measuring the power.

•We start with an initial guessed value of “a” which is the center-to-center separation between the slits.

•The inner PID loop is repeated 5 times after which the outer learning loop comes into effect.

•The learning loop updates the estimated set points and tracks the actual set point. In this simulation, the learning algorithm was run 28 times.

We track “m” to be 1.86 which relates to a slit width “a” of 32um.

eSm TLearning Equation :

Initial X Encoder Position = 10477 Final X Encoder Position = 10810

Initial Y Encoder position = 24Final Y Encoder position = 25

Thus, we show an increase in power level reached along with increased efficiency and accuracy.