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Entry  Wed Nov 28 10:42:47 2018, anchal, Notes, TempCtrl, Temperature control efforts in last few days Screenshot_from_2018-11-26_16-32-54.png
    Reply  Wed Nov 28 19:50:20 2018, rana, Notes, TempCtrl, Temperature control efforts in last few days 
Message ID: 2258     Entry time: Wed Nov 28 10:42:47 2018     Reply to this: 2259
Author: anchal 
Type: Notes 
Category: TempCtrl 
Subject: Temperature control efforts in last few days 

Tying the estimated PID coefficients:

From the configuration run (see PSL:2257), I estimated some new PID coefficients. I left PID on for the long weekend with the new coefficient with the initial point about 5 MHz off from lock set point. Apparently, the system never converged (see the orange trace in attached screenshot of StripTool). It instead kept oscillating.

At this point, it seems that maybe the PID control won't work good enough. We need to keep beatnote frequency within 10kHz of setpoint to reach desired resolution range and avoid jumps in Marconi. We either need a new way of temperature control, or if PID works nearby setpoint with no hard changes, we need to reach there with almost zero slope.

Trying out a new method for driving frequency:

This week I started working on a new method to drive the frequency to setpoint with near to zero first derivative. The purpose of this method is to reach close enough to setpoint in a calm manner so that PID can function nicely. If it works out better, it can be used as primary control near setpoint as well.

Plan Schematic:

There would be three codes working together to achieve temperature driving (and/or control).

  • DataMiner.py: This code will constantly monitor beat note frequency and calculate second derivatives with data sampled at 10 Hz, 1 Hz and 100 mHz. It will keep writing this data to a knowledge file. Also, this code will look for signs of beatnote frequency flipping (cavity frequencies crossing eachother which reversed required sign of actuation). These second derivatives will be logged with the applied actuation signal. Idea is to create overtime a file which represents what happens to beatnote frequency when heat is applied.
  • DataInterpreter.py: This code will read the knowledge file and fit it to \frac{d^2 f_{BN}}{dt^2} = c_0 + c_1 h + c_2 h^2 where f_{BN} is beatnote frequency and h is applied differential heat in watts. This is the simplest non-trivial model for effect of actuation. We might to think further about this part. The fitted coefficients (for both signs of beatnote frequency, so 6 in total) will be written on EPICS channels.
  • ChimpLocker.py: This code will fit a fifth order polynomial track to reach the setpoint frequency from present frequency. The data points it would use would for this fitting would be Inital Frequency, Initial Slope, Setpoint Frequency, Setpoint Slope (always set to 0) and time required to reach setpoint. The last variable will be chosen to make sure actuation remain in limits. This is the part which a simple PID lacks. Then, the coefficients from DataInterpreter.py would be used to calculate the differential heat required to effect the right second derivatives to create the projected track. If the frequency deviates from track, a new track would be calculated.

This plan is in an infant stage. We might need to change a lot of things and models depending on what data is acquired. So, for now, to reduce development time (with a possible chance of failure), I've only incorporated the DataMiner code which is on Git/cit_ctnlab/ctn_scripts/ now. I've also written a DataMinerHelper.py which spans the actuation space slowly while keeping frequency in range so that good knowledge data can be mined. These two codes are near final release.

Depending on what the knowledge data looks like, we will decide if we should go further with this plan. Until then, I'll work on noiseBudget code again.

Minor update:

I have reconfigured the ufc-6000 to have a sampling rate of 0.1 Hz which is minimum possible from design. I have changed ufc2.py to make sure it is set to this sampling rate everytime we read precavity beatnote frequency data.

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