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In my area, every controls engineer says "we can just use a PID".

99% of the time it seems they're not wrong, despite control engineering being a pretty large area of research.

And a simple PID controller built with op-amps looks an awful lot like a simple neural network.

http://www.ecircuitcenter.com/Circuits/op_pid/op_pid.htm

https://www.nutsvolts.com/magazine/article/the_perceptron_ci...

Isn't gradient descent basically PID over parameters? And tricks like momentum basically a low-pass filter integrated in the PID loop? It's quite weird how not that many concepts from analog electronics domain have gotten carried over to ML.

I'm nominally familiar with PID loops but am not a control engineer. What other tools would you commonly bring to bear when a PID is not applicable?

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