CS452 - Real-Time Programming - Spring 2008

Lecture 29 - Practical Control and Calibration

Questions & Comments

1. How to give a demo

Practical Control

Data

What data do you have?

• train number
• speed requested
• track segment
• length of track segment
• absolute times at ends

How do you find out where the train is?

• last sensor hit, plus
• (travel time since last sensor) * velocity

We need to use the data to get a good estimate of the velocity, which may be a function of

• speed requested
• train number
• track segment
• time because of train degradation

This data has three types of errors

1. screw-up errors
   1. throw them out
   2. sometimes you can eliminate them
2. random errors
   • average them out
   • often you can turn random errors into systematic ones
3. systematic errors
   • project them out

How useful is yesterday's data?

Eliminating screw-up errors

Redefine the track

For example, if a sensor malfunctions frequently

• combine the two track segments into one

Transforming random errors

You can sometimes identify patterns in what you think are random errors

• e.g., you have one speed calibration for curved segments
• another for straight segments
• and discover that segments with switches are different.

Subdivide the data.

• Do this as little as possible, but not too little

Processing Data

Averaging

Subdivide data into cells, average within cells.

Averaging techniques

1. whole series: average' = average * (n-1)/n + data / n
2. moving window, varying
   • size
   • weighting

   equal weighting: average' = average + (new-data - dropped-data)/n

Extrapolating

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