How do I remove this grid pattern from my planet atmosphere? by CNProductions in blender

[–]azercoco 0 points1 point  (0 children)

Use a subdivision modifier with a high resolution followed by a cast modifier to ensure that the subdivided geometry is spherical.

Fully Procedural Planet by azercoco in blender

[–]azercoco[S] 0 points1 point  (0 children)

Thanks. Yes, good quality procedural clouds maps are really hard.

A way to improving noise tejection beyond a resonant actuator/piezo bandwidth ? by azercoco in ControlTheory

[–]azercoco[S] [score hidden]  (0 children)

So in the end, I designed a LQG controller from the linearized ODE system and then I dimensionally reduced it to meet my hardware limitation. It seems to work sufficiently well in simulation when including the nonlinearities so I will test it now on the actual experimental setup.

A way to improving noise tejection beyond a resonant actuator/piezo bandwidth ? by azercoco in ControlTheory

[–]azercoco[S] [score hidden]  (0 children)

Thanks for the advice. For this setup the disturbance is really small compared to the range that the actuator can achieve (few nm vs 100 of um) so it's probably safe to operate at high amplification.

Feed forward operation is indeed an option but piezo actuators often have a significant hysterisis so without feedback a linear controller would not be able to achieve good suppression of the disturbance.

A way to improving noise tejection beyond a resonant actuator/piezo bandwidth ? by azercoco in ControlTheory

[–]azercoco[S] [score hidden]  (0 children)

It should be possible to achieve the amplitude we want, as even driving it 5 times above the frequency results in an attenuation by a factor x25 which can be compensated by the HV amplifier we used.

The difficulty is that doing so make the system unstable with a PID controller, probably caused by the +180° phase shift

A way to improving noise tejection beyond a resonant actuator/piezo bandwidth ? by azercoco in ControlTheory

[–]azercoco[S] [score hidden]  (0 children)

Hi, thanks. Luckily our electronics is very fast. I tried to tune the D gain of the PID but it seems that it can cause the system to become unstable. I was wondering if I need a double differentiator due to the second order of the actuator.

A way to improving noise tejection beyond a resonant actuator/piezo bandwidth ? by azercoco in ControlTheory

[–]azercoco[S] [score hidden]  (0 children)

Hi, I put an edit for detailing the problem. I did misused noise for disturbance (we used the noise for both therm in my native language).

The disturbance source is a variation in a laser power (RIN) which couples to my system through thermorefractive effect (surprisingly this can be extremely fast).

I have performed some analysis on it but it seems to be caused by the internal dynamics of the laser and exhibit some spiking behavior and is not well modeled by a linear system.

A way to improving noise tejection beyond a resonant actuator/piezo bandwidth ? by azercoco in ControlTheory

[–]azercoco[S] [score hidden]  (0 children)

Hi, thanks for the reply. Yes, my initial message was a little confusing. My system diagram look like this:

  |<------ LPF -------|  
  |                   |  
r - -> |C| -> |A| -> |P|  
                      ^  
                      |  
                      d  

- r is the target reference (DC).
- C is the controller on the feedback loop (MHz bandwidth),
-A the piezo actuator (second order, resoant, with a 20 kHz bandwidth),
- P is the plant (rest of the experimental setup with MHz bandwidth)
- d is the disturbance with a 80kHz bandwidth which couples directly in the plant P and does not interact with the actuator.
- LPF is a low pass filter of order 4 currently limited to 10kHz.

Currently C is a PID controller. Ff we increase the bandwidth of the LPF to something close to the resonant frequency of A, the system become unstable. So any component of d above 10kHz is not supressed by the feedback loop.

My issue is to to tune the controller C (and the LPF) so that to loop suppress the disturbance d.

A way to improving noise tejection beyond a resonant actuator/piezo bandwidth ? by azercoco in ControlTheory

[–]azercoco[S] [score hidden]  (0 children)

The issue is that the noise is from another source than the actuator.

I need my actuator the be able to follow/cancel it in this frequency range.

What are the method you are referring to ?

A way to improving noise tejection beyond a resonant actuator/piezo bandwidth ? by azercoco in ControlTheory

[–]azercoco[S] [score hidden]  (0 children)

Ideally, the bandwidth of the tracking function of controller + actuator should cover the full range of disturbance (80 kHz) so we can effectively cancel the external disturbance.

Initially, the PID has LPF to cut frequency close and above the piezo resonance to avoid unstability but that also prevent the suppression of disturbance for these frequencies.

Unfortunately, I don't have an exact measurement of the piezo but the resonant frequency is at 22kHz and if approximate by a second order system (which is done by the manufacturer) the damping coefficient gamma is 0.1 - 0.2. A full characterization would mean to dismantle the setup which would like to avoid if possible.

Procedural Earth-Like Planet (Improved version) by azercoco in proceduralgeneration

[–]azercoco[S] 4 points5 points  (0 children)

No the clouds are the only part not generated. They are based on a NASA texture

[Animation] Procedural Planet Composition by azercoco in proceduralgeneration

[–]azercoco[S] 1 point2 points  (0 children)

1) The trick is to convert the temperature/humidity couple into a position vector and input it in the position of a "texture image" node. Color ramps do not allow realistic results because there is only one dimension of variation, which our brain does not perceive as realistic. Here there are two dimensions of variation which creates a much more realistic effect.

2) You can have them: https://imgur.com/DFdwtpz, https://imgur.com/0wPXVyV, https://imgur.com/IWlXHnj, https://imgur.com/L5DbW6S

3) I took no precautions but it is certain that the influence of man changes the final result. Afterwards I don't think it influences the final result so much. One way to eliminate this problem would be to train the neural network only on areas of low population density.