ELOG 40m

Front-End Assembly and Testing

The channels on both the C1BHD and C1SUS2 seem to be frozen: they arent updating and are holding one value. To fix this Anchal and I tried:

restarting the computers
- restarting basically everything including the models
Changing the matrix values
adding filters
messing with the offset
restarting the network ports (Paco suggested this apparently it worked for him at some point)
Checking to make sure everything was still connected inside the case (DAC, ADC, etc..)

I wonder if Jon has any ideas.

16224

Thu Jun 24 17:32:52 2021

Front-End Assembly and Testing

Anchal and I ran tests on the two systems (C1-SUS2 and C1-BHD). Attached are the results and the code and data to recreate them.

We connected one DAC channel to one ADC channel and thus all of the results represent a DAC/ADC pair. We then set the offset to different values from -3000 to 3000 and recorded the measured signal. I then plotted the response curve of every DAC/ADC pair so each was tested at least once.

There are two types of plots included in the attachments

1) a summary plot found on the last pages of the pdf files. This is a quick and dirty way to see if all of the channels are working. It is NOT a replacement for the other plots. It shows all the data quickly but sacrifices precision.

2) In an in-depth look at an ADC/DAC pair. Here I show the measured value for a defined DC offset. The Gain of the system should be 0.5 (put in an offset of 100 and measure 50). I included a line to show where this should be. I also plotted the difference between the 0.5 gain line and the measured data.

As seen in the provided plots the channels get saturated after about the -2000 to 2000 mark, which is why the difference graph is only concentrated on -2000 to 2000 range.

Summary: all the channels look to be working they all report very little deviation off of the theoretical gain.

Note: ADC channel 31 is the timing signal so it is the only channel that is wildly off. It is not a measurement channel and we just measured it by mistake.

Attachment 1: C1-SU2_Channel_Responses.pdf

Attachment 2: C1-BHD_Channel_Responses.pdf

Attachment 3: CDS_Channel_Test.zip

16230

Wed Jun 30 14:09:26 2021

I have looked at my code from the previous plot of the transfer function and realized that there is a slight error that must be fixed before we can analyze the difference between the theoretical transfer function and the measured transfer function.

The theoretical transfer function, which was generated from Photon has approximately 1000 data points while the measured one has about 120. There are no points between the two datasets that have the same frequency values, so they are not directly comparable. In order to compare them I must infer the data between the points. In the previous post [16195] I expanded the measured dataset. In other words: I filled in the space between points linearly so that I could compare the two data sets. Using this code:

#make values for the comparison

tck_mag = splrep(tst_f, tst_mag) # get bspline representation given (x,y) values

gen_mag = splev(sim_f, tck_mag) # generate intermediate values

dif_mag=[]

for x in range(len(gen_mag)):

    dif_mag.append(gen_mag[x]-sim_mag[x]) # measured minus predicted



tck_ph = splrep(tst_f, tst_ph) # get bspline representation given (x,y) values

gen_ph = splev(sim_f, tck_ph) # generate intermediate values

dif_ph=[]

for x in range(len(gen_ph)):

    dif_ph.append(gen_ph[x]-sim_ph[x])

At points like a sharp peak where the measured data set was sparse compared to the peak, the difference would see the difference between the intermediate “measured” values and the theoretical ones, which would make the difference much higher than it really was.

To fix this I changed the code to generate the intermediate values for the theoretical data set. Using the code here:

tck_mag = splrep(sim_f, sim_mag) # get bspline representation given (x,y) values

gen_mag = splev(tst_f, tck_mag) # generate intermediate values

dif_mag=[]

for x in range(len(tst_mag)):

    dif_mag.append(tst_mag[x]-gen_mag[x])#measured minus predicted



tck_ph = splrep(sim_f, sim_ph) # get bspline representation given (x,y) values

gen_ph = splev(tst_f, tck_ph) # generate intermediate values

dif_ph=[]

for x in range(len(tst_ph)):

    dif_ph.append(tst_ph[x]-gen_ph[x])

Because this dataset has far more values (about 10 times more) the previous problem is not such an issue. In addition, there is never an inferred measured value used. That makes it more representative of the true accuracy of the real transfer function.

This is an update to a previous plot, so I am still using the same data just changing the way it is coded. This plot/data does not have a Q of 1000. That plot will be in a later post along with the error estimation that we talked about in this week’s meeting.

The new plot is shown below in attachment 1. Data and code are contained in attachment 2

Attachment 1: SingleSusPlantTF.pdf

Attachment 2: Plant_TF_Test.zip

16289

Mon Aug 23 15:25:59 2021

I am adding a State-space block to the SimPlant cds model using the example Chris gave. I made a new folder in controls called SimPlantStateSpace. wI used the code below to make a state-space LTI model with a 1D pendulum then I converted it to a discrete system using c2d matlab function. Then I used these in the rtss.m file to create the state space code I need in the SimPlantStateSpace_1D_model.h file.

%sys_model.m

Q = 1000; phi = 1/Q; g = 9.806; m = 0.24; % mass of pendulum l = 0.248; %length of pendulum w_0 = sqrt(g/l);

f=16000 %this is the frequency of the channel that will be used

A = [0 1; -w_0^2*(1+1/Q*1i) -w_0/Q] B = [0; 1/m]; C = [1 0]; D = [0]; sys_dc = ss(A,B,C,D)

sys=c2d(sys_dc, 1/f)

This code outputs the discrete state space that is added to the header file attached.

Attachment 1: SimPlantStateSpace.zip

16339

Thu Sep 16 14:08:14 2021

Frogs

Tour

I gave some of the data analysts a look around because they asked and nothing was currently going on in the 40m. Nothing was changed.

16348

Mon Sep 20 15:42:44 2021

Quantization Code Summary

This post serves as a summary and description of code to run to test the impacts of quantization noise on a state-space implementation of the suspension model.

Purpose: We want to use a state-space model in our suspension plant code. Before we can do this we want to test to see if the state-space model is prone to problems with quantization noise. We will compare two models one for a standard direct-ii filter and one with a state-space model and then compare the noise from both.

Signal Generation:

First I built a basic signal generator that can produce a sine wave for a specified amount of time then can produce a zero signal for a specified amount of time. This will let the model ring up with the sine wave then decay away with the zero signal. This input signal is generated at a sample rate of 2^16 samples per second then stored in a numpy array. I later feed this back into both models and record their results.

State-space Model:

The code can be seen here

The state-space model takes in the list of excitation values and feeds them through a loop that calculates the next value in the output.

Given that the state-space model follows the form

$\dot{x}(t)=\textbf{A}x(t)+ \textbf{B}u(t)$ and $y(t)=\textbf{C}x(t)+ \textbf{D}u(t)$ ,

the model has three parts the first equation, an integration, and the second equation.

The first equation takes the input x and the excitation u and generates the x dot vector shown on the left-hand side of the first state-space equation.
The second part must integrate x to obtain the x that is found in the next equation. This uses the velocity and acceleration to integrate to the next x that will be plugged into the second equation
The second equation in the state space representation takes the x vector we just calculated and then multiplies it with the sensing matrix C. we don't have a D matrix so this gives us the next output in our system

This system is the coded form of the block diagram of the state space representation shown in attachment 1

Direct-II Model:

The direct form 2 filter works in a much simpler way. because it involves no integration and follows the block diagram shown in Attachment 2, we can use a single difference equation to find the next output. However, the only complication that comes into play is that we also have to keep track of the w(n) seen in the middle of the block diagram. We use these two equations to calculate the output value

$y[n]=b_0 \omega [n]+b_1 \omega [n-1] +b_2 \omega [n-2]$ , where w[n] is $\omega[n]=x[n] - a_1 \omega [n-1] -a_2 \omega[n-2]$

Bit length Control:

To control the bit length of each of the models I typecast all the inputs using np.float then the bit length that I want. This simulates the computer using only a specified bit length. I have to go through the code and force the code to use 128 bit by default. Currently, the default is 64 bit which so at the moment I am limited to 64 bit for highest bit length. I also need to go in and examine how numpy truncates floats to make sure it isn't doing anything unexpected.

Bode Plot:

The bode plot at the bottom shows the transfer function for both the IIR model and the state-space model. I generated about 100 seconds of white noise then computed the transfer function as

$G(f) = \frac{P_{csd}(f)}{P_{psd}(f)}$

which is the cross-spectral density divided by the power spectral density. We can see that they match pretty closely at 64 bits. The IIR direct II model seems to have more noise on the surface but we are going to examine that in the next elog

Attachment 1: 472px-Typical_State_Space_model.svg.png

Attachment 2: Biquad_filter_DF-IIx.svg.png

Attachment 3: SS-IIR-TF.pdf

16355

Wed Sep 22 14:22:35 2021

Here is the Code that follows the following procedure (as of today at least)

Now that we have a model of how the SS and IIR filters work we can get to the problem of how to measure the quantization noise in each of the systems. Den Martynov's thesis talks a little about this. from my understanding: He measured quantization noise by having two filters using two types of variables with different numbers of bits. He had one filter with many more bits than the second one. He fed the same input signal to both filters then recorded their outputs x_1 and x_2, where x_2 had the higher number of bits. He then took the difference x_1-x_2. Since the CDS system uses double format, he assumes that quantization noise scales with mantissa length. He can therefore extrapolate the quantization noise for any mantissa length.

This problem is a little harder than I had originally thought. I took Rana's advice and asked Aaron about how he had tackled a similar problem. We came up with a procedure explained below (though any mistakes are my own):

Feed different white noise data into three of the same filter this should yield the following equation: $\textbf{S}_i^2 =\textbf{S}_{ni}^2+\textbf{S}_x^2$ , where $\textbf{S}_i^2$ is the power spectrum of the output for the ith filter, $\textbf{S}_{ni}^2$ is the noise filtered through an "ideal" filter with no quantization noise, and $\textbf{S}_x^2$ is the power spectrum of the quantization noise. Since we are feeding random noise into the input the power of the quantization noise should be the same for all three of our runs.
Next, we have our three outputs: $\textbf{S}_1^2$ , $\textbf{S}_2^2$ , and $\textbf{S}_3^2$ that follow the equations:

$\textbf{S}_1^2 =\textbf{S}_{n1}^2+\textbf{S}_x^2$

$\textbf{S}_2^2 =\textbf{S}_{n2}^2+\textbf{S}_x^2$

$\textbf{S}_3^2 =\textbf{S}_{n3}^2+\textbf{S}_x^2$

From these three equations, we calculate the three quantities: $\textbf{S}_{12}^2$ , $\textbf{S}_{23}^2$ , and $\textbf{S}_{13}^2$ which are calculated by:

$\textbf{S}_{12}^2 =\textbf{S}_{1}^2-\textbf{S}_2^2\approx \textbf{S}_{n1}^2 -\textbf{S}_{n2}^2$

$\textbf{S}_{23}^2 =\textbf{S}_{2}^2-\textbf{S}_3^2\approx \textbf{S}_{n2}^2 -\textbf{S}_{n3}^2$

$\textbf{S}_{13}^2 =\textbf{S}_{1}^2-\textbf{S}_3^2\approx \textbf{S}_{n1}^2 -\textbf{S}_{n3}^2$

from these quantities, we can calculate three values: $\bar{\textbf{S}}_{n1}^2$ , $\bar{\textbf{S}}_{n2}^2$ , and $\bar{\textbf{S}}_{n3}^2$ since these are just estimates we are using a bar on top. These are calculated using:

$\bar{\textbf{S}}_{n1}^2\approx\frac{1}{2}(\textbf{S}_{12}^2+\textbf{S}_{13}^2+\textbf{S}_{23}^2)$

$\bar{\textbf{S}}_{n2}^2\approx\frac{1}{2}(-\textbf{S}_{12}^2+\textbf{S}_{13}^2+\textbf{S}_{23}^2)$

$\bar{\textbf{S}}_{n3}^2\approx\frac{1}{2}(\textbf{S}_{12}^2+\textbf{S}_{13}^2-\textbf{S}_{23}^2)$

using these estimates we can then estimate $\textbf{S}_{x}^2$ using the formula:

$\textbf{S}_{x}^2 \approx \textbf{S}_{1}^2 - \bar{\textbf{S}}_{n1}^2 \approx \textbf{S}_{2}^2 - \bar{\textbf{S}}_{2}^2 \approx \textbf{S}_{3}^2 - \bar{\textbf{S}}_{n3}^2$

we can average the three estimates for $\textbf{S}_{x}^2$ to come up with one estimate.

This procedure should be able to give us a good estimate of the quantization noise. However, in the graph shown in the attachments below show that the noise follows the transfer function of the model to begin with. I would not expect this to be true so I believe that there is an error in the above procedure or in my code that I am working on finding. I may have to rework this three-corner hat approach. I may have a mistake in my code that I will have to go through.

I would expect the quantization noise to be flatter and not follow the shape of the transfer function of the model. Instead, we have what looks like just the result of random noise being filtered through the model.

Next steps:

The first real step is being able to quantify the quantization noise but after I fix the issues in my code I will be able to start liking at optimal model design for both the state-space model and the direct form II model. I have been looking through the book "Quantization noise" by Bernard Widrow and Istvan Kollar which offers some good insights on how to minimize quantization noise.

Attachment 1: IIR64-bitnoisespectrum.pdf

16360

Mon Sep 27 12:12:15 2021

I have not been able to figure out a way to make the system that Aaron and I talked about. I'm not even sure it is possible to pull the information out of the information I have in this way. Even the book uses a comparison to a high precision filter as a way to calculate the quantization noise:

"Quantization noise in digital filters can be studied in simulation by comparing the behavior of the actual quantized digital filter with that of a refrence digital filter having the same structure but whose numerical calculations are done extremely accurately."
-Quantization Noise by Bernard Widrow and Istvan Kollar (pg. 416)

Thus I will use a technique closer to that used in Den Martynov's thesis (see appendix B starting on page 171). A summary of my understanding of his method is given here:

A filter is given raw unfiltered gaussian data $f(t)$ then it is filtered and the result is the filtered data $x(t)$ thus we get the result: $f(t)\rightarrow x(t)=x_N(t)+x_q(t)$ where $x_N(t)$ is the raw noise filtered through an ideal filter and $x_q(t)$ is the difference which in this case is the quantization noise. Thus I will input about 100-1000 seconds of the same white noise into a 32-bit and a 64-bit filter. (hopefully, I can increase the more precise one to 128 bit in the future) then I record their outputs and subtract the from each other. this should give us the Quantization error $e(t)$ :
$e(t)=x_{32}(t)-x_{64}(t)=x_{N_{32}}(t)+x_{q_{32}}(t) - x_{N_{64}}(t)-x_{q_{64}}(t)$
and since $x_{N_{32}}(t)=x_{N_{64}}(t)$ because they are both running through ideal filters:
$e(t)=x_{N}(t)+x_{q_{32}}(t) - x_{N}(t)-x_{q_{64}}(t)$
$e(t)=x_{q_{32}}(t) -x_{q_{64}}(t)$
and since in this case, we are assuming that the higher bit-rate process is essentially noiseless we get the Quantization noise $x_{q_{32}}(t)$ .

If we make some assumptions, then we can actually calculate a more precise version of the quantization noise:

"Since aLIGO CDS system uses double precision format, quantization noise is extrapolated assuming that it scales with mantissa length"
-Denis Martynov's Thesis (pg. 173)

From this assumption, we can say that the noise difference between the 32-bit and 64-bit filter outputs: $x_{q_{32}}(t)-x_{q_{64}}(t)$ is proportional to the difference between their mantissa length. by averaging over many different bit lengths, we can estimate a better quantization noise number.

I am building the code to do this in this file

16361

Mon Sep 27 16:03:15 2021

I have coded up the procedure in the previous post: The result does not look like what I would expect.

As shown in Attachment1 I have the power spectrum of the 32-bit output and the 64-bit output as well as the power spectrum of the two subtracted time series as well as the subtracted power spectra of both. unfortunately, all of them follow the same general shape of the raw output of the filter.

I would not expect quantization noise to follow the shape of the filter. I would instead expect it to be more uniform. If anything I would expect the quantization noise to increase with frequency. If a high-frequency signal is run through a filter that has high quantization noise then it will severely degrade: i.e. higher quantization noise.

This is one reason why I am confused by what I am seeing here. In all cases including feeding the same and different white noise into both filters, I have found that the calculated quantization noise is proportional to the response of the filter. this seems wrong to me so I will continue to play around with it to see if I can gain any intuition about what might be happening.

Attachment 1: DeltaNoiseSpectrum.pdf

16366

Thu Sep 30 11:46:33 2021

First and foremost I have the updated bode plot with the mode moved to 10 Hz. See Attachment 1. Note that the comparison measurement is a % difference whereas in the previous bode plot it was just the difference. I also wrapped the phase so that jumps from -180 to 180 are moved down. This eliminates massive jumps in the % difference.

Next, I have two comparison plots: 32 bit and 64bit. As mentioned above I moved the mode to 10 Hz and just excited both systems at 3.4283Hz with an amplitude of 1. As we can see on the plot the two models are practically the same when using 64bits. With the 32bit system, we can see that the noise in the IIR filter is much greater than in the State-space model at frequencies greater than our mode.

Note about windowing and averaging: I used a Hanning window with averaging over 4 neighbor points. I came to this number after looking at the results with less averaging and more averaging. In the code, this can be seen as nperseg=num_samples/4 which is then fed into signal.welch

Attachment 1: SS-IIR-Bode.pdf

Attachment 2: PSD_32bit.pdf

Attachment 3: PSD_64bit.pdf

16403

Thu Oct 14 16:38:26 2021

Kicking optics in freeSwing measurment

General

[Ian, Anchal]

We are going to kick the optics tonight at 2am.

The optics we will kick are the PRM BS ITMX ITMY ETMX ETMY

We will kick each one once and record for 2000 seconds and the log files will be placed in users/ian/20211015_FreeSwingTest/logs.

16406

Fri Oct 15 12:14:27 2021

Kicking optics in freeSwing measurment

General

[Ian, Anchal]

we ran the free swinging test last night and the results match up with in 1/10th of a Hz. We calculated the peak using the getPeakFreqs2 script to find the peaks and they are close to previous values from 2016.

In attachment 1 you will see the results of the test for each optic.

The peak values are as follows:

Optic	POS (Hz)	PIT (Hz)	YAW (Hz)	SIDE (Hz)
PRM	0.94	0.96	0.99	0.99
MC2	0.97	0.75	0.82	0.99
ETMY	0.98	0.98	0.95	0.95
MC1	0.97	0.68	0.80	1.00
ITMX	0.95	0.68	0.68	0.98
ETMX	0.96	0.73	0.85	1.00
BS	0.99	0.74	0.80	0.96
ITMY	0.98	0.72	0.72	0.98
MC3	0.98	0.77	0.84	0.97

The results from 2016 can be found at: /cvs/cds/rtcdt/caltech/c1/scripts/SUS/PeakFit/parameters2.m

Attachment 1: 20211015_Kicktest_plot.pdf

16414

Tue Oct 19 18:20:33 2021

c1sus2 DAC to ADC test

I ran a DAC to ADC test on c1sus2 channels where I hooked up the outputs on the DAC to the input channels on the ADC. We used different combinations of ADCs and DACs to make sure that there were no errors that cancel each other out in the end. I took a transfer function across these channel combinations to reproduce figure 1 in T2000188.

As seen in the two attached PDFs the channels seem to be working properly they have a flat response with a gain of 0.5 (-6 dB). This is the response that is expected and is the result of the DAC signal being sent as a single ended signal and the ADC receiving as a differential input signal. This should result in a recorded signal of 0.5 the amplitude of the actual output signal.

The drop off on the high frequency end is the result of the anti-aliasing filter and the anti-imaging filter. Both of these are 8-pole elliptical filters so when combined we should get a drop off of 320dB per decade. I measured the slope on the last few points of each filter and the averaged value was around 347dB per decade. This is slightly steeper than expected but since it is to cut off higher frequencies it shouldn't have an effect on the operation of the system. Also it is very close to the expected value.

The ripples seen before the drop off are also an effect of the elliptical filters and are seen in T2000188.

Note: the transfer function that doesn't seem to match the others is the heartbeat timing signal.

Attachment 1: data3_Plots.pdf

Attachment 2: data2_Plots.pdf

16423

Fri Oct 22 17:35:08 2021

Particle counter setup near BS Chamber

PEM

I have done some reading about where would be the best place to put the particle counter. The ISO standard (14644-1:2015) for cleanrooms is one every 1000 m^2 so one for every 30m x 30m space. We should have the particle counter reasonably close to the open chamber and all the manufactures that I read about suggest a little more than 1 every 30x30m. We will have it much closer than this so it is nice to know that it should still get a good reading. They also suggest keeping it in the open and not tucked away which is a little obvious. I think the best spot is attached to the cable tray that is right above the door to the control room. This should put it out of the way and within about 5m of where we are working. I ordered some cables to route it over there last night so when they come in I can put it up there.

16430

Tue Oct 26 18:24:00 2021

c1sus2 DAC to ADC test

[Ian, Anchal, Paco]

After the Koji found that there was a problem with the power source Anchal and I fixed the power then reran the measurment. The only change this time around is that I increased the excitation amplitude to 100. In the first run the excitation amplitude was 1 which seemed to come out noise free but is too low to give a reliable value.

link to previous results

The new plots are attached.

Attachment 1: data2_Plots.pdf

Attachment 2: data3_Plots.pdf

16447

Wed Nov 3 16:55:23 2021

[Ian, Tega, Raj]

This is the rough plan for the testing of the new suspension models with the created plant model. We will test the suspensions on the plant model before we implement them into the full

Get State-space matrices from the surf model for the SOS. Set up simplant model on teststand
- The state-space model is only 3 degrees of freedom. (even the surf's model)
- There are filter modules that have the 6 degrees of freedom for the suspensions. We will use these instead. I have implemented them in the same suspension model that would hold the state space model. If we ever get the state space matrices then we can easily substitute them.
Load new controller model onto test stand. This new controller will be a copy of an existing suspension controller.
Hook up controller to simplant. These should form a closed loop where the outputs from the controller go into the plant inputs and the plant outputs go to the controller inputs.
Do tests on set up.
- Look at the step response for each degree of freedom. Then compare them to the results from an existing optic.
  - Also, working with Raj let him do the same model in python then compare the two.
Make sure that the data is being written to the local frame handler.

MEDM file location

/opt/rtcds/userapps/release/sus/common/medm/hsss_tega_gautam

run using

For ITMX display, use:

hsss_tega_gautam>medm -x -macro "site=caltech,ifo=c1,IFO=C1,OPTIC=ITMX,SUSTYPE=IM,DCU_ID=21,FEC=45" SUS_CUST_HSSS_OVERVIEW.adl

16457

Mon Nov 8 17:52:22 2021

Setting up suspension test model

[Ian, Tega]

We combined a controler and a plant model into a single modle (See first attachment) called x1sus_cp.mdl in the userapps folder of the cymac in c1sim. This model combines 2 blocks: the controler block which is used to control the current optics and is found in cvs/cds/rtcds/userapps/release/sus/c1/models/c1sus.mdl further the control block we are using comes from the same path but from the c1sup.mdl model. This plant model is the bases for all of my custom plant models and thus is a good starting point for the testing. It is also ideal because I know it can beeasily altered into a my state-space plant model. However, we had to make a few adjustments to get the model up to date for the cds system. So it is now a unique block.

These two library blocks are set in the userapps/lib folder on the cymac. This is the lib file that the docker system looks to when it is compiling models. For a quick overview see this. All other models have been removed from the MatLab path so that when we open x1sus_cp.mdl in MatLab it is using the same models it will compile with.

We could not find the rtbitget library part, but chris pointed us to userapps, and we copied it over using: scp /opt/rtcds/userapps/trunk/cds/common/models/rtbitget.mdl controls@c1sim:/home/controls/simLink/lib.

NOTE TO FUTURE IAN: don't forget that unit delays exist.

Next step: now that we have a model that is compiling and familiar we need to make medm screens. We will use the auto mdl2adl for this so that it is quick. Then we can start adding our custom pieces one by one so that we know that they are working. We will also work with Raj to get an independent python model working. Which will allow us to compare the cds and python models.

Attachment 1: x1sus_cp.png

16460

Tue Nov 9 13:40:02 2021

[Ian, Tega]

After talking with Rana we have an updated plan. We will be working on this plan step by step in this order.

Remove c1sim from the test stand rack and move it to the rack in the office next to the printer. When connecting it we will NOT connect it to the Martian network! This is to make sure that nothing is connected to the 40m system and we can't mess anything up.
Once we have moved the computer over physically, we will need to update anyone who uses it on how to connect to it. The way we connect to it will have changed.
Now that we have the computer moved and everyone can connect to it we will work on the model. Currently, we have the empty models connected.
1. recompile the model since we moved the computer.
2. verify that nothing has changed in the move and the model can still operate and compile properly
The model has the proper structure but we need to fill it with the proper filters and such
1. For the Plant model
  1. To get it up and running quickly we will use the premade plant filters for the plant model. These filters were made for the c1sup.mdl and should work in our modified plant model. This will allow us to verify that everything is working. And allow us to run tests on the system.
  2. We need to update the model and add the state space block. (we are skipping this step for now because we are fast-tracking the testing)
    1. Check with Chris to make sure that this is the right way to do it. I am pretty sure it is, but I don't know anything
    2. Make the 6 DOF state-space matrix. We only have a three DOF one. The surf never made a 6 DOF.
    3. Make the block to input into the model
    4. make a switch that will allow us to switch between the state-space model and the filter block
2. For the controller
  1. Load filter coefficients for the controller model from one of the current optics and use this as a starting point.
  2. Add medm screens for the controller and plant. We are skipping this for now because we want results and we don't care if the screens look nice and are useable at the moment.
Test the model
1. we will take an open-loop transfer function of all six of the DOFs to all other DOFs which will leave us with 36 TFs. Many will be zero
  1. If you are looking at this post then we are measuring transfer functions from the blue flags to the green flags across the plant model.
  2. We will want to look at the TFs across the controller

16461

Tue Nov 9 16:55:52 2021

[Ian, Tega]

We have moved c1sim computer from the test stand to the server rack in the office area. (see picture)

It is connected to the general campus network. Through the network switch at the top of the rack. This switch seeds the entire Martian network.

Test to show that I am not lying:

you can ping it or ssh into it at
controls@131.215.114.116
Using the same password as before. Notice this is not going through the nodus network.
It also has a different beginning of the IP addresses. Martian network IP addresses start with 191.168.113

c1sim is now as connected to the 40m network as my mom's 10-year-old laptop.

unfortunately, I have not been able to get the x2go client to connect to it. I will have to investigate further. It is nice to have access to the GUI of c1sim occasionally.

Attachment 1: IMG_8107.JPG

16462

Tue Nov 9 18:05:03 2021

see this post for more detail

[Ian, Tega]

Now that the computer is in its new rack I have copied over the filter two files that I will use in the plant and the controller from pianosa:/opt/rtcds/caltech/c1/chans to the docker system in c1sim:/home/controls/docker-cymac/chans. That is to say, C1SUP.txt -> X1SUP.txt and C1SUS.txt -> X1SUS_CP.txt, where we have updated the names of the plant and controller inside the txt files to match our testing system, e.g. ITMX -> OPT_PLANT in plant model and ITMX -> OPT_CTRL in the controller and the remaining optics (BS, ITMY, PRM, SRM) are stripped out of C1SUS.txt in order to make X1SUS_CP.txt.

Once the filter files were copied over need to add them to the filters that are in my models to do this I run the commands:

$  cd docker-cymac

$  eval $(./env_cymac)

$  ./login_cymac

 #  cd /opt/rtcds/tst/x1/medm/x1sus_cp

 #  medm -x X1SUS_OPT_PLANT_TM_RESP.adl

Unfortunately, the graphics forwarding from the docker is not working and is giving the errors:

arg: X1SUS_OPT_PLANT_TM_RESP.adl

locateResource 'X1SUS_OPT_PLANT_TM_RESP.adl'

isNetworkRequest X1SUS_OPT_PLANT_TM_RESP.adl

canAccess('X1SUS_OPT_PLANT_TM_RESP.adl', 4) = 0

can directly access 'X1SUS_OPT_PLANT_TM_RESP.adl'

isNetworkRequest X1SUS_OPT_PLANT_TM_RESP.adl

locateResource(X1SUS_OPT_PLANT_TM_RESP.adl...) returning 1

Error: Can't open display:

This means that the easiest way to add the filters to the model is through the GUI that can be opened through X2go client. It is probably easiest to get that working. graphics forwarding from inside the docker is most likely very hard.

unfortunately again x2go client won't connect even with updated IP and routing. It gives me the error: unable to execute: startkde. Going into the files on c1sim:/usr/bin and trying to start startkde by myself also did not work, telling me that there was no such thing even though it was right in front of me.

16466

Mon Nov 15 15:12:28 2021

[Ian, Tega]

We are working on three fronts for the suspension plant model:

Filters
1. We now have the state-space matrices as given at the end of this post. From these matrices, we can derive transfer functions that can be used as filter inputs. For a procedure see HERE. We accomplish this using Matlab's built-in ss(A,B,C,D); function. then we make it discrete using c2d(sys, 1/f); this gives us our discrete system running at the right frequency. We can get the transfer functions of either of these systems using tf(sys);
2. from there we can copy the transfer functions into our photon filters. Tega is working on this right now.
State-Space
1. We have our matrices as listed at the end of this post. With those compiled into a discrete system in MatLab we can use the code Chris made called rtss.m to convert this system into a .c file and a .h file.
2. from there we have moved those files under the userapps folder in the docker system. then we added a c-code block to our .mdl model for the plant and pointed it at the custom c file we made. See section 7.2 of T080135-v10
3. We have done all this and this should implement a custom state-space function into our .mdl file. the downside of this is that to change our SS model we have to edit the matrices we can't edit this from an medm screen. We have to recompile every time.
Python Check
1. This python check is run by Raj and will take in the state-space matrices which are given then will take transfer functions along all inputs and outputs and will compare them to what we have from the CDS model.

Here are the State-space matrices:

$A=\begin{bmatrix} 0 & 1 & 0 & 0 & 0 & 0 & 0 & 0 \\ -\omega_x^2(1+i/Q_{x}) & -\gamma_x & \omega_xb & 0 & 0 & 0 & 0 & 0 \\ 0 & 0 & 0 & 1 & 0 & 0 & 0 & 0 \\ \frac{\omega_{\theta}^2}{l+b} & 0 & -\omega_{\theta}^2(1+i/Q_{\theta}) & -\gamma_{\theta} & 0 & 0 & 0 & 0 \\ 0 & 0 & 0 & 0 & 0 & 1 & 0 & 0 \\ 0 & 0 & 0 & 0 & -\omega_{\phi}^2(1+i/Q_{\phi}) & -\gamma_{\phi} & 0 & 0 \\ 0 & 0 & 0 & 0 & 0 & 0 & 0 & 1 \\ 0 & 0 & 0 & 0 & 0 & 0 & -\omega_{y}^2(1+i/Q_{y}) & -\gamma_{y}\end{bmatrix}$

$B=\begin{bmatrix} 0 & 0 & 0 & 0 \\ \frac{1}{m} & 0 & 0 & 0 \\ 0 & 0 & 0 & 0 \\ 0 & \frac{R_m}{I_{\theta}} & 0 & 0 \\ 0 & 0 & 0 & 0 \\ 0 & 0 & \frac{R_m}{I_{\phi}} & 0 \\ 0 & 0 & 0 & 0 \\ 0 & 0 & 0 & \frac{1}{m} \end{bmatrix}$ $C=\begin{bmatrix} 1 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\ 0 & 0 & 1 & 0 & 0 & 0 & 0 & 0 \\ 0 & 0 & 0 & 0 & 1 & 0 & 0 & 0 \\ 0 & 0 & 0 & 0 & 0 & 0 & 1 & 0 \end{bmatrix}$ $D=\begin{bmatrix} 0 & 0 & 0 & 0 \\ 0 & 0 & 0 & 0 \\ 0 & 0 & 0 & 0 \\ 0 & 0 & 0 & 0 \end{bmatrix}$

A few notes: If you want the values for these parameters see the .yml file or the State-space model file. I also haven't been able to find what exactly this s is in the matrices.

UPDATE [11/16/21 4:26pm]: I updated the matrices to make them more general and eliminate the "s" that I couldn't identify.

The input vector will take the form:

$\begin{bmatrix} x \\ \dot{x} \\ \theta \\ \dot{\theta} \\ \phi \\ \dot{\phi} \\ y \\ \dot{y} \end{bmatrix}$

where x is the position, theta is the pitch, phi is the yaw, and y is the y-direction displacement

16469

Tue Nov 16 17:29:49 2021

[Ian, Tega]

Updated A, B, C, D matrices for the state-space model to remove bugs in the previous estimate of the system dynamics. Updated the last post to represent the current matrixes.

We used MatLab to get the correct time-series filter coefficients in ZPK format and added them to the filters running in the TM_RESP filter matrix.

Get the pos-pos transfer function from the CDS model. Strangely, this seems to take a lot longer than anticipated to generate the transfer function, even though we are mainly probing the low-frequency behavior of the system.

For example, a test that should be taking approximately 6 minutes is taking well over an hour to complete. This swept sine (results below) was on the low settings to get a fast answer and it looks bad. This is a VERY basic system it shouldn't be taking this long to complete a Swept sine TF.

Noticed that we need to run eval $(./env_cymac) every time we open a new terminal otherwise CDS doesn't work as expected. Since this has been the source of quite a few errors already, we have decided to put it in the startup .bashrc script.

loc=$(pwd)

cd ${HOME}/docker-cymac/

eval $(./env_cymac)

cd ${loc}

Attachment 1: x_x_TF1.pdf

16477

Thu Nov 18 20:00:43 2021

[Ian, Raj, Tega]

Here is the comparison between the results of Raj's python model and the transfer function measurement done on the plant model by Tega and me.

As You can see in the graphs there are a few small spots of disagreement but it doesn't look too serious. Next we will measure the signals flowing through the entire plant and controller.

For a nicer (and printable) version of these plots look in the zipped folder under Plots/Plant_TF_Individuals.pdf

Attachment 1: Final_Plant_Testing.zip

16481

Wed Nov 24 11:02:23 2021

I added mpmath to the quantization noise code. mpmath allows me to specify the precision that I am using in calculations. I added this to both the IIR filters and the State-space models although I am only looking at the IIR filters here. I hope to look at the state-space model soon.

Notebook Summary:

I also added a new notebook which you can find HERE. This notebook creates a signal by summing two sine waves and windowing them.

Then that signal is passed through our filter that has been limited to a specific precision. In our case, we pass the same signal through a number of filters at different precisions.

Next, we take the output from the filter with the highest precision, because this one should have the lowest quantization noise by a significant margin, and we subtract the outputs of the lower precision filters from it. In summary, we are subtracting a clean signal from a noisy signal; because the underlying signal is the same, when we subtract them the only thing that should be left is noise. and since this system is purely digital and theoretical the limiting noise should be quantization noise.

Now we have a time series of the noise for each precision level (except for our highest precision level but that is because we are defining it as noiseless). From here we take a power spectrum of the result and plot it.

After this, we can calculate a frequency-dependent SNR and plot it. I also calculated values for the SNR at the frequencies of our two inputs.

This is the procedure taken in the notebook and the results are shown below.

Analysis of Results:

The first thing we can see is that the precision levels 256 and 128 bits are not shown on our graph. the 256-bit signal was our clean signal so it was defined to have no noise so it cant be plotted. The 128-bit signal should have some quantization noise but I checked the output array and it contained all zeros. after further investigation, I found that the quantization noise was so small that when the result was being handed over from mpmath to the general python code it was rounding those numbers to zero. To overcome this issue I would have to keep the array as a mpmath object the entire time. I don't think this is useful because matplotlib probably couldn't handle it and it would be easier to just rewrite the code in C.

The next thing to notice is sort of a sanity check thing. In general, low precision filters yield higher noise than high precision. This is a good quick sanity check. However, this does not hold true at the low end. we can see that 16-bit actually has the highest noise for most of the range. Chris pointed out that at low precisions that quantization noise can become so large that it is no longer a linearly coupled noise source. He also noted that this is prone to happen for low precision coefficients with features far below the Nyquist frequency like I have here. This is one explanation that seems to explain the data especially because this ambiguity is happening at 16-bit and lower as he points out.

Another thing that I must mention, even if it is just a note to future readers, is that quantization noise is input dependent. by changing the input signal I see different degrees of quantization noise.

Analysis of SNR:

One of the things we hoped to accomplish in the original plan was to play around with the input and see how the results changed. I mainly looked at how the amplitude of the input signal scaled the SNR of the output. Below I include a table of the results. These results were taken from the SNR calculated at the first peak (see the last code block in the notebook) with the amplitude of the given sine wave given at the top of each column. this amplitude was given to both of the two sine waves even though only the first one was reported. To see an example, currently, the notebook is set up for measurement of input amplitude 10.

	0.1 Amplitude of input	1 Amplitude	100 Amplitude	1000 Amplitude
4-bit SNR	5.06e5	5.07e5	5.07e5	5.07e5
8-bit SNR	5.08e5	5.08e5	5.08e5	5.08e5
16-bit SNR	2.57e6	8.39e6	3.94e6	1.27e6
32-bit SNR	7.20e17	6.31e17	1.311e18	1.86e18
64-bit SNR	6.0e32	1.28e32	1.06e32	2.42e32
128-bit SNR	unknown	unknown	unknown	unknown

As we can see from the table above the SNR does not seem to relate to the amplitude of the input. in multiple instances, the SNR dips or peaks in the middle of our amplitude range.

Attachment 1: PSD_IIR_all.pdf

16492

Tue Dec 7 10:55:25 2021

[Ian, Tega]

Tega and I have gone through the IIR Filter code and optimized it to make sure there aren't any areas that force high precision to be down-converted to low precision.

For the new biquad filter we have run into the issue where the gain of the filter is much higher than it should be. Looking at attachments 1 and 2, which are time series comparisons of the inputs and outputs from the different filters, we see that the scale for the output of the Direct form II filter shown in attachment 1 on the right is on the order of 10^-5 where the magnitude of the response of the biquad filter is on the order of 10^2. other than this gain the responses look to be the same.

I am not entirely sure how this gain came into the system because we copied the c code that actually runs on the CDS system into python. There is a gain that affects the input of the biquad filter as shown on this slide of Matt Evans Slides. This gain, shown below as g, could decrease the input signal and thus fix the gain. However, I have not found any way to calculate this g.

With this gain problem we are left with the quantization noise shown in Attachment 4.

State Space:

I have controlled the state space filter to act with a given precision level. However, my code is not optimized. It works by putting the input state through the first state-space equation then integrating the result, which finally gets fed through the second state-space equation.

This is not optimized and gives us the resulting quantization noise shown in attachment 5.

However, the state-space filter also has a gain problem where it is about 85 times the amplitude of the DF2 filter. Also since the state space is not operating in the most efficient way possible I decided to port the code chris made to run the state-space model to python. This code has a problem where it seems to be unstable. I will see if I can fix it

Attachment 1: DF2_TS.pdf

Attachment 2: BIQ_TS.pdf

Attachment 4: PSD_COMP_BIQ_DF2.pdf

Attachment 5: PSD_COMP_SS_DF2.pdf

16498

Fri Dec 10 13:02:47 2021

I am trying to replicate the simulation done by Matt Evans in his presentation (see Attachment 1 for the slide in particular).

He defines his input as $x_{\mathrm{in}}=sin(2\pi t)+10^{-9} sin(2\pi t f_s/4)$ so he has two inputs one of amplitude 1 at 1 Hz and one of amplitude 10^-9 at 1/4th the sampling frequency in this case: 4096 Hz

For his filter, he uses a fourth-order notch filter. To achieve this filter I cascaded two second-order notch filters (signal.iirnotch) both with locations at 1 Hz and quality factors of 1 and 1e6. as specified in slide 13 of his presentation.

I used the same procedure outlined here. My results are posted below in attachment 2.

Analysis of results:

As we can see from the results posted below the results don't match. there are a few problems that I noticed that may give us some idea of what went wrong.

First, there is a peak in the noise around 35 Hz. this peak is not shown at all in Matt's results and may indicate that something is inconsistent.

the second thing is that there is no peak at 4096 Hz. This is clearly shown in Matt's slides and it is shown in the input spectrum so it is strange that it does not appear in the output.

My first thought was that the 4kHz signal was being entered at about 35Hz but even when you remove the 4kHz signal from the input it is still there. The spectrum of the input shown in Attachment 3 shows no features at ~35Hz.

The Input filter, Shown in attachment 4 shows the input filter, which also has no features at ~35Hz. Seeing how the input has no features at ~35Hz and the filter has no features at ~35Hz there must be either some sort of quantization noise feature there or more likely there is some sort of sampling effect or some effect of the calculation.

To figure out what is causing this I will continue to change things in the model until I find what is controlling it.

I have included a Zip file that includes all the necessary files to recreate these plots and results.

Attachment 1: G0900928-v1_(dragged).pdf

Attachment 2: PSD_COMP_BIQ_DF2.pdf

Attachment 3: Input_PSD.pdf

Attachment 4: Input_Filter.pdf

Attachment 5: QuantizationN.zip

16675

Tue Feb 22 18:47:51 2022

[Ian, Koji]

In preparation for the replacement of the suspension electronics that control the ETMY, I took measurments of the system excluding the CDS System. I took transfer functions from the input to the coil drivers to the output of the OSEMs for each sensor: UL, UR, LL, LR, and SIDE. These graphs are shown below as well as all data in the compressed file.

We also had to replace the oplev laser power supply down the y-arm. The previous one was not turning on. the leading theory is that it's failure was caused by the power outage. We replaced it with one Koji brought from the fiber display setup.

I also am noting the values for the OSEM DC output

OSEM	Value
UL	557
UR	568
LR	780
LL	385
SIDE	328

In addition the oplev position was:

~~OPLEV_POUT~~	~~4.871~~
~~OPLEV_YOUT~~	~~-0.659~~
OPLEV_PERROR	-16.055
OPLEV_YERROR	-6.667

(KA ed) We only care about PERROR and YERROR (because P/YOUT are servo output)

Edit: corrected DC Output values

Attachment 1: ALL_TF_Graph.pdf

Attachment 2: 20220222_SUSElectronicsReplacement.7z

16680

Fri Feb 25 14:00:08 2022

[Koji, Ian]

We looked at a few power supplies and we found one that was marked "CHECK IF THIS WORKS" in yellow. We found that the power supply worked but the indicator light didn't work. I tried a two other lights from other power supplies that did not work but they did not work. Koji ordered a new one.

Attachment 1: IMG_0853.jpg

Attachment 2: IMG_0852.jpg

16681

Fri Feb 25 14:48:53 2022

I moved the network-enabled power strip from above the power supplies on rack 1y4 to below. Nothing was powered through the strip when I unplugged everything and I connected everything to the same port after.

Attachment 1: Before.jpg

Attachment 2: After.jpg

16687

Mon Feb 28 15:51:07 2022

ETMY 1Y4 Electronics Replacement

[Paco, Ian]

paco helped me wire the ETMY 1Y4 rack. We wired the following (copied from Koji's email):

Use DB9-DB9 to complete the wiring between
1. 16bit DAC AI Chassis - End DAC Adapter (4 cables)
2. End DAC Adapter - HAM-A Coil Driver (2 cables)
3. AA Chassis - End ADC Adapter (2 cables)
Koji already brought two special DB9-DB15 cables (in plastic bags) to the end. They connect the HAM-A coil drivers to the satellite amp. At this time, we skip Low Noise HV Bias Driver.
Bring two 30ft DB25 (called #1, aka D2100675-01) cables from the office area to the end. I collected one end and left them there.
All the new units have +/-18V DC supply in the back. Find the orange cables behind the 40m vacuum duct around Y-end and connect the units and the DC power strip. Use short cables if possible to save the longer ones.

the cables we used:

Number Used	Type of Cable	Length
8	DB9 to DB9	2.5 ft
2	DB9 to DB9	5 ft
2	DB9-DB15
2	DB25 (called #1, aka D2100675-01)	30ft
9	Orange Power Cables	~ 3 ft

I attached pictures below.

Attachment 1: IMG_0865.jpg

Attachment 2: IMG_0867.jpg

Attachment 3: IMG_0868.jpg

Attachment 4: IMG_0866.jpg

16699

Thu Mar 3 17:21:11 2022

ETMY 1Y4 Electronics Replacement

[Koji, Ian]

1) We attached the 30 coil driving cables to the vacuum feed through to the sat amp [40m wiki] they run along the cable tray then up and down into the rack.

2) we checked all DB and power cables. We found that the anti-imaging filter had a short and got very hot when plugged in. the back power indicator lights turned on fine but the front panel stayed off. We removed it and replaced it with the one that was on the test stand marked for the BHD. This means we need to fix the broken one and Koji mentioned getting another one.

3) we reassigned the ADC and DAC channels in the iscey model and the asy model. we committed a version before we made any changes.

4) Finally we tested the setup to make sure the ETM was being damped.

Next step:

1) Measure the change of the PD gains and the actuator gains. See pervious elog

16709

Mon Mar 7 16:44:15 2022

Now that the ETMSUS is back up and running I reran my measurements from the beginning of the process. The results below show a change in gain between the before and after measurements. I have given values of the low-frequency section below.

Average Gain difference from the TFs: 18.867 (excluding thee side change)

	Before	After	Gain difference
UL	-31 dB	-5 dB	19.952
UR	-36 dB	-10 dB	19.952
LR	-27 dB	-2 dB	17.782
LL	-37 dB	-12 dB	17.782
SIDE	-48 dB	-45 dB	1.412

I also am noting the new values for the OSEM DC output: average gain increase: 9.004

OSEM	DC OFFSET Before	DC OFFSET After	Gain increase
UL	557	5120	9.19
UR	568	5111	8.99
LR	780	7041	9.02
LL	385	3430	8.91
SD	328	2922	8.91

In addition, the oplev position was:

	Before	After
OPLEV_PERROR	-16.055	-16.715
OPLEV_YERROR	-6.667	-16.597

All data and settings have been included in the zip file

From the average gain increase of the TFs which indicates the increase of the whole system and the increase in gain from the OSEM we can calculate the gain from the actuators.

18.867/9.004 = 2.09

thus the increase in gain on the actuator is about 2.09

EDIT: I updated the side TF with one with better SNR. I increased the excitation amplitude.

Attachment 1: UR_TF_Graph.pdf

Attachment 2: UL_TF_Graph.pdf

Attachment 3: LR_TF_Graph.pdf

Attachment 4: LL_TF_Graph.pdf

Attachment 5: SD_TF_Graph.pdf

Attachment 6: 20220307_SUSElectronicsAfterTests.zip

16723

Fri Mar 11 16:43:03 2022

I updated the gain of the ct2um filter on the OSEMS for ETMY and decreased their gain by a factor of 9 from 0.36 to 0.04.

I added a filter called "gain_offset" to all the coils except for side and added a gain of 0.48.

together these should negate the added gain from the electronics replacement of the ETMY

16727

Tue Mar 15 13:49:44 2022

I have received the new screwdriver bits that will work with the two electric screwdrivers we have. I have distributed them in the 40m. Some are in the electronics stations and some are in the toolbox in the lab. The new electric screwdriver (which looks like a drill but takes typical screwdriver bits) is in the room with the workshop. It is in the blue Makita box. For some reason, lots of the old bits were rounded because of incorrect use. I have thrown the unuseable ones out.

I also requested some screw extractors in case we need them. the one we have now is really big and may not work on smaller screws.

16739

Mon Mar 21 18:00:05 2022

BHD

XARM AUX alignment cont.

[Ian, Paco]

Continuing with the previous alignment that we stoped on friday, we re set up my heavily cleaned iPhone on FaceTime. The Phone alowed us to see the laser on the ITMX and center it on that optic.

We increased the trigger level for the watchdog on the optics.
Green beam was centered on th ITMX.
aligned the green laser: prompt reflection off of the ETMX to maximize the signal on the reflection PD
Went back to ITMX chamber, looked for the beam in the tube to see if the laser was hitting the beam tube off of the ITMX. From there we comunicated how to adjust the beam to move the beam towards the ETMX.
On the ETMX the reflection PD signal was measured and the ITMX was adjusted using CDS until ripples/"flashes" were seen in the reflection PD channel (C1:ALS-X_REFL_DC_OUT_DQ).
Once we saw flashing we tried to minimize the reflection pd signal using only ITMX alignment slider so that all the light was being held in the cavity.
Once we had this going the PDH lock engaged the higher order mode that we were seeing.
We removed the phone setup and closed up the ITMX chamber.
From here we moved to the control room and continude to adjust the ITMX and ETMX to see if we could lock to a lower order mode.
- This proved unsuccessful so we will resume by trying to replicate the ASS action manually (with M1 - M2 PZT sliders and the REFL PD)

Attachment 1: PXL_20220322_015247138.jpg

16746

Mon Mar 28 16:25:34 2022

ETMY SUS Electronics Replacement - Questions

The point of changing the gains was to return the system to its origional state. ie I wanted the over all gain of the physical components to be the same as when we started. From the CDS side of things nothing else should be changed. The damping filters should remain at their origional values. The cts2um filter was changed to counteract a change in the electronics (replacing them). These changes should cancel eachother out. As for the side control, on 3/4/22 koji reduced the output resistors for the 4 face OSEMs but did not change the the SD one. there fore the SD did not need the same adjustment as the others.

Quote:

After Ian updated the cts2um filters for OSEM, shouldn't the damping gains be increased back by factor of 10 to previos values? Was the damping gain for SIDE ever changed? we found it at 250.

Can you explain why gain_offset filter was required and why this wasn't done for the side coil?

Quote:

I updated the gain of the ct2um filter on the OSEMS for ETMY and decreased their gain by a factor of 9 from 0.36 to 0.04.

I added a filter called "gain_offset" to all the coils except for side and added a gain of 0.48.

together these should negate the added gain from the electronics replacement of the ETMY

16749

Thu Mar 31 18:58:16 2022

Part IIa of BHR upgrade - IR laser alignment on Xarm

BHD

[Ian, Paco]

We continued to try and lock the IR laser to the x-arm. Last time we adjusted TT2 PR2 and PR3 to adjust the beam to hit the center of the ITMX. Today we continued to adjust the same optics to make the beam hit the center of the beam splitter and make sure the beam is going in the direction of the ITMY. Then removed a mirror that wasn't even bolted down. I put it on the front right corner of the hood with all the other optics (right in front of a suspended mirror). With this unknown mirror removed the path was clear and hit the center of ITMX. From there we went and opened the ETMX chamber and using the IR scope we adjusted ITMX to put the beam on the center of the ETMX. We continued adjusting ETMX and ITMX until we had three reflections. We stopped here because we realized we should set up POX. We think this because any adjustment to set up the POX would mess up the work we had done on the x arm alignment.

16750

Fri Apr 1 14:26:19 2022

Particle counter setup near BS Chamber

PEM

I mounted the particle counter over the BS chamber attached to the cable tray as seen in Attachment 1. The signal cable runs through an active 30ft cable to the 1x2 rack. the wire is labeled and runs properly through the cable tray. The particle counter is plugged in at the power strip attached near the cable tray. The power cord is also labeled.

I restarted the particle counter service in the c1psl computer in the /etc/systemd/system/ folder using the commands

sudo systemctl restart particleCounter

sudo systemctl status particleCounter

I cannged the usb hub assigned in the service file to ttyUSB0 which is what we saw the computer had named it.

Checking the channels from this elog show the same particle count as when testing with the buttons and checking the screen. It seems that the channels had been down but are now restarted.

Attachment 1: IMG_1407.jpg

16768

Fri Apr 8 17:21:31 2022

Part IIa of BHR upgrade - IR laser alignment on Yarm

BHD

[Ian, Paco, Tega]

Paco and I opened the ETMY and ITMY chamber to work on yesterdays efforts to lock the y arm. We temporarily in stalled a camera behind the ETMY to look at the transmission as we adjusted the ETMY and ITMY. We then moved on to setting up the POY. the beam was too large for the apature of the PD so we installed a lens in the beam path to decrease it.

Once that was installed we saw some flashing on the C1:LSC-POYCD_OUT channel. We also could see the flashing on the monitors in the control room. The flashing beam seemed to be in the middle of ITMY but was slightly to the right on the ETMY. From here we tried to walk the beam using PR3 and ETMY to move the beam to the center of the ETMY.

16774

Wed Apr 13 15:57:25 2022

Tested the Nikon batteries for the camera. they are supposed to be 7V batteries but they don't hold a charge. I confirmed this with multi-meter after charging for days. Ordered new ones Nikon EN-EL9

16775

Wed Apr 13 16:23:54 2022

[Ian, Paco, JC]

There is a strange smell in the 40m. It smells like a chemically burning smell maybe like a shorted component. I went around with the IR camera to see if anything was unusually hot but I didn't see anything. The smell seems to be concentrated at the vertex and down the y-arm

16783

Mon Apr 18 14:52:47 2022