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.

https://mitp-content-server.mit.edu/books/content/sectbyfn/b...

But the benefit of PID is it is fairly easy to tune and works in a really wide range of situations.

> PIDs are great but notoriously hard to tune. They require deep insight into the underlying physical phenomena to get right. They are also rather rigid and cannot adjust well to a changing environment (temperature and humidity can fluctuate dramatically between summer and winter in some climates).

This is not true. PID controllers are often the least dependent on the physical characteristics. They can be tuned with heuristic methods like Ziegler-nichols, often with no knowledge of the actual system.

> Of course, no one tunes them by hand anymore for these reasons, relying instead on optimization techniques like particle swarm to find the best set of coefficients for a given steady state condition.

This is also not true. In the Amazon consumer robotics group we still tuned pid by hand. I’ve _never_ heard of tuning pid with particle swarm, that seems very silly, difficult, and overkill. If you’re going to use an optimization technique, you might as well move to a better controller structure like LQR. I have seen particle swarm used as an estimator, as an alternative to a kalman filter, but never seen it used for tuning.

> Eventually, I suspect we will replace most PIDs with a small neural network for almost all industrial applications (a handful of nodes is sufficient). The neural network is also easier to adapt to changing conditions.

This sounds unlikely to me. Classic control techniques give guarantees that a neural net just can’t. For example, things are provably stable under some assumptions. With a neural net you get no such guarantee. Also, it would be harder to debug and understand, and it would take more memory and compute. I can’t imagine a world where we replace pid with neural nets, they’re fit for very different purposes.

Source: have a masters in controls, worked in robotics in controls team, still do consulting in this area when I have time, and I love it all.

With or without serverless lambda architecture bitcoins ?

in the industry, maybe. Otherwise, FRC competitions are seeing a LOT of manually tuned PIDs

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).

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.

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For example, I will upvote this submission, but not your comment.