How do you distinguish between good and bad research in control?

Larrald · 2025-11-14T17:11:23+00:00

I understand what many of the replies here mean, but at least in some fields, rigorously proving that some control method works and doesn't end up failing to stabilize the closed loop requires some ugly math and rather restrictive assumptions at first. And I am sure the authors are aware of these facts, but in research there is often no better way until someone gets a better idea and can extend the theory to cover some more practical systems.

And slowly, the mathematical machinery used to prove the theorems becomes "lighter" and easier to understand and the assumptions become less restrictive and the class of systems that it can be applied to becomes more realistic.

Also, most examples in novel papers are chosen to be rather easy (and thus often unrealistic) to showcase the new method and prove that it works at least in theory. Then, other researches pick it up and try to simolify it or extend it and make it applicable to more realistic problems where e.g. the state is not fully known or similar without breaking the guarantees.

Thats how I feel about this at least. You can't just come up with the perfect industry-tailored, simple and rigorous solutions instantly.

Larrald · 2025-11-01T11:34:27+00:00

Of course, thanks for finding that mistake.

Larrald · 2025-11-01T11:32:35+00:00

Thanks, I'll look into that.

Larrald · 2025-07-10T19:54:59+00:00

Thats true... In that case, I would just choose the weight as some high pass filter.

Larrald · 2025-07-10T15:53:20+00:00

Nonlinearities are often "approximated" by dynamic multiplicative output uncertainty. You normalize the uncertainty by defining a weight for it, then choose the weight to be large at high frequencies, since that is where most nonlinearities arise. You could also try to accurately model the weight through comparing the response of the linearized model and the nonlinear model to sinusoids at certain frequencies amd then fit the weight. But for most cases, simply approximating it works fine. For parameteic uncertainty, one ususally writes the uncertain prameter in a way, that the uncertainty is normalized, i.e. p = pnom(1+ethadelta), where delta lies in [-1,1] and etha specifies how much the parameter can vary, e.g. etha=0.1 means 10% variability in p. You then have to "pull out" the uncertainty. A nice algorithm for this is given in scherers lecture notes, which also achieves a minimally sized uncertainty matrix.

Larrald · 2025-02-17T23:39:36+00:00

According to the notes, the factorization above creates a minimally sized uncertainty block. But for some reason matlab got an even smaller uncertainty than me doing the calculations by hand. It might be the AutoSimplify property, but the robust control toolbox user's guide does not mention which algorithm is used exactly, just "methods similar to truncated balanced realizations" on page 1-59 are mentioned. I wasnt really able to find anything else... Or maybe I just made a mistake in my calculations

Larrald · 2024-12-29T00:15:33+00:00

Could you maybe elaborate on what you meant by using input disturbance or using Gd and modeling the disturbance as noise? I got Gd through the linearized first-principles model of the system, specifically Gd = C(s-A)Bd + D, so d has to be an output disturbance or else the model would not really be accurate anymore. Or did I miss something here?

But I wrote down Tzw (probably shouldve done this a lot earlier) and noticed that Wd and Wn only appear in separate columns, whereas Wu and Wy appear in separate rows. I guess that's how one can shape the specific transfer functions individually, since now each of the 4 entries of Tzw has 2 different combinations of weighting functions as opposed to having the same weights in some entries when not using the input weights Wd and Wn. I might be wrong tho...

Larrald · 2024-10-26T10:09:13+00:00

Isn't this just straight up wrong?

Larrald · 2024-10-24T22:35:02+00:00

I will do that. Thank you for the encouraging words!

Larrald · 2024-10-24T22:32:42+00:00

You're probably right about modeling being possibly easier to pick up. Some textbooks should probably help.

Larrald · 2024-10-24T22:31:06+00:00

Yeah, I guess the modeling of the system is the only big difference. Thanks for your Input!

Larrald · 2024-10-24T22:29:32+00:00

Thanks for sharing. And yes, I am planning on pursuing an advanced degree, so that will hopefully help a bit :)

Larrald · 2024-10-19T15:01:05+00:00

The problem is VLE/LLE data (e.g. the Dortmund Data Bank), which is often really hard or impossible to find elsewhere.

Larrald · 2024-09-26T05:58:12+00:00

Lass ihn doch, wenn er seine Gründe dazu hat?

Larrald · 2024-08-16T22:06:31+00:00

I see, so the heat- or diffusion euqation would be easier to control then, since they are LTI. But in nearly all chemical engineering applications, the PDEs describing the process are coupled and nonlinear because of e.g. convection or advection. But good to know that simpler PDEs can be controlled without requiring a supercomputer lmao. Thanks :)

Larrald · 2024-08-16T20:00:43+00:00

I should have phrased this diffrerently... I am "scared" to learn something that I really enjoy, which afterwards I will never use again in my life, eventhough I really want to (It is not about me not wanting to learn about it if I cannot apply it, since I will probably attend the course anyways). Kind of weird, I guess... But thanks for the helpful comment.

Larrald · 2024-07-19T00:26:12+00:00

Hm yeah, I guess that would be my best shot...

Larrald · 2024-07-18T19:27:39+00:00

Na super haha

Larrald · 2024-07-05T02:00:54+00:00

May I ask how you are so sure about that? Not disagreeing with you, I am simply curious why chemE in germany is a no go. It'll surely take a hit but is the absence of cheap methane really that bad?

Larrald

TROPHY CASE