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Message ID: 12564     Entry time: Fri Oct 14 19:59:09 2016
Author: Yinzi 
Type: Update 
Category: Green Locking 
Subject: Continuing work with the TC 200 

Oct. 15, 2016

Another attempt (following elog 8755) to extract the oven transfer function from time series data using Matlab’s system identification functionalities.

The same time series data from elog 8755 was used in Matlab’s system identification toolbox to try to find a transfer function model of the system.

From elog 8755: H(s) is known from current PID gains: H(s) = 250 + 60/s +25s, and from the approximation G(s)=K/(1+Ts), we can expect the transfer function of the system to have 3 poles and 2 zeros.

I tried fitting a continuous-time and a discrete time transfer function with 3 poles and 2 zeros, as well as using the "quick start" option. Trying to fit a discrete time transfer function model with 3 poles and 2 zeros gave the least inaccurate results, but it’s still really far off (13.4% fit to the data).

Ideas:

1. Obtain more time domain data with some modulation of the input signal (also gives a way to characterize nonlinearities like passive cooling). This can be done with some minor modifications to the existing code on the raspberry pi. This should hopefully lead to a better system ID.

2. Try iterative tuning approach (sample gains above and below current gains?) so that a tune can be obtained without having to characterize the exact behavior of the heater.

Oct. 16, 2016

-Found the raspberry pi but it didn’t have an SD card

-Modified code to run directly on a computer connected to the TC 200. Communication seems to be happening, but a UnicodeDecodeError is thrown saying that the received data can’t be decoded.

-Some troubleshooting: tried utf-8 and utf-16 but neither worked. The raw data coming in is just strings of K’s, [‘s, and ?’s

-Will investigate possible reasons (update to Mac OS or a difference in Python version?), but it might be easier to just find an SD card for the raspberry pi which is known to work. In the meantime, modify code to obtain more time series data with variable input signals.

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