It follows that

1 / 1

# It follows that - PowerPoint PPT Presentation

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.

I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.

## PowerPoint Slideshow about 'It follows that' - abby

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.

- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript

Real System

Input

Output

+

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.

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

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

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

= 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

and

Step5:

Itfollows that

Identification Flowchart

Example System

• 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.

Manual

Fiber-Fiber Alignment

Semi-Automatic

• 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.

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

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

Experimental Results

Theory and Simulation

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

( m is unknown and K is known )

The variables u, y, and are to be measured

Optical setup

Amplifiers, Encoders

Interpolators, Motion Controller

{ }

Step 1:

and

{ Assume estimated model and }

Step 2:

The Sensitivity coefficients are contained in

where

Step 3:

Inaccurate modeling could lead to deviation from the actual values.

Learning Equation :

• Activated at a lower sampling frequency.
• Provides opportunities for the system to improve upon its power model.
• Adjust the accuracy on the basis of “experienced evidence.”

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

Visit www.ece.drexel.edu/opticslab/results/learning.wmv to watch the Learning Identification Video

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

Criteria for choosing e

If e

is too large, the schemes will diverge.

Initial X Encoder Position = 10477

Final X Encoder Position = 10810

Initial Y Encoder position = 24

Final Y Encoder position = 25

If e is too small, then

will approach

very slowly.

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

Algorithm

• Comparison of Power Levels
• Current State-of-the-Art = 0.644 uW
• Our Technique: Model Based Control = 1.55 uW
• Our Technique: Learning Identification Control = 1.675 uW

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

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