Posted by dhorthy 4 days ago
Still uses a PID for BLDC motor coil current control, as this control loop is much more predictable.
Currently using the proportional part only from a PID for position control, but this may change in the future.
[0] https://en.wikipedia.org/wiki/Active_disturbance_rejection_c... (although this isn't a very useful reference if you want the implementation maths!)
But the interference from the PWM signal appeared to mess with the speed sensor readouts that I couldn't make it work.
Can what you describe be used to solve it? If you would be kind enough to describe it, that would be great.
I think that PID hits a certain sweet spot between cleverness, ease of implementation and practical utility that makes it catnip for the typical programmer's mind.
I liked it so much that when we had to implement it, I downloaded an open source driving simulator to see it work there instead of the simpler python environment we were using.
It's easy to implement, but hard to tune.
PID controllers can be built from analog pneumatic components, and often are.[1] This predates computer control. The I term is called "Reset" and the D term is called "Rate" in classical control.
[1] https://control.com/textbook/closed-loop-control/pneumatic-p...
Sounds like he made a bag with the first AI craze and retired.
It's sold as something completely different though that will revolutionize autonomous decision-making which it clearly isn't, it's just trying to re-invent the wheel but with neural nets this time around.
I'll confess I didn't understand what you meant with the part of your comment after the semi-colon.
https://mitp-content-server.mit.edu/books/content/sectbyfn/b...
A common hiring anecdote we share with people outside tech is literally: “A CS degree doesn’t teach you how to code.”
For me, ~25 years ago in the UC system, it was all math/science/theory-oriented. Some C++/Java that was introduced to get you through all that theory. Learning how to code/actual software engineering comes with practical experience.
(I am quite happy to have gotten the software engineering education.)
Keep in mind that plenty of people on HN and in the industry did not take CS degrees in college. We did learn about PIDs, if briefly.
But the benefit of PID is it is fairly easy to tune and works in a really wide range of situations.
I do sort of suspect a fan thermal control curve is a PID response curve written out in long hand but don't really have the math to prove it.
No, a fan response curve is kinda-sorta a P controller. It does not take into account 1) how quickly the temperature is rising or dropping (D) 2) the time passed since the system has drifted from the target temp (I).
No futzing with software needed.
Reminds me of Dave "EEVblog" Jones' in his "always give negative feedback" T-shirt.
(for those who don't know: it's a reference to opamps)
My computer sits next to my desk, so I want it to be as quiet as possible. This means the fan should run as the lowest speed which maintains a reasonable temperature, so at the very least there should be some kind of mapping between temperature and fan speed.
However, a rapidly-changing fan speed results in a more annoying sound than a slightly faster fan, and chip temperatures can ramp fast, so a simple linear mapping between temperature and fan speed isn't enough. To get a quiet fan in an environment where you are, say, loading the CPU to 100% for 100ms every second, the fan controller should be able to average out the heat production and let the fan run at a steady speed. After all, the cooler is a big chunk of metal, so its temperature will never ramp as fast as the core itself, so a rapid fan speed response is pointless.
Whether it gets upvoted depends on a lot of things.
For example, I will upvote this submission, but not your comment.
I still learn a lot from wikipedia. Maybe it's not useful for those who are experts in every thing. I am not one of them.
Not that I am complaining, I come to this place to learn cool stuff, and there isn’t enough of it.
The actual PID controllers ,however, are actually really finicky to tune, and hard to reason about directly. Good for simple linear systems, but it falls apart as real life systems tend to be complex and nonlinear.