[ncallaway]

What's often hard about this situation is tuning to one problem case (fixing that downhill drift) often makes a different scenario worse. For example, fixing the downhill drift can make that overshoot worse.

I can imagine tuning something like this for a cruise control scenario would be tricky, because there's probably hundreds of different cases to test for.

I think a simulator here could be particularly fun to toy with, because you could have "unit tests" with 200 different scenarios that you try to optimize for. You could set your parameters, and it could run the 200 different scenarios, and show you were your parameters did well and where they did badly.

[jacques_chester]

There's a lot of literature spread across a bunch of fields. The one I first saw was "Active Nonlinear Tests"[0] from the systems dynamics / complex systems area. The idea is that you perform searches of parameter space for a simualation to find the places where it exhibits dramatic nonlinearities.

I've since seen something similar but not identical in the Statistical Process Control world: robust process (or product) design[1]. The idea is that any given system can be thought of as a function that represents X = F(a, b, c, ...) + E, where E is some measure of error or noise that can't be removed. The goal is to find settings for a, b, c etc that minimise the effect of E. So that even if uncontrolled noise swirls around, your process remains stable. You can imagine flipping that to look for maxima instead.

[0] https://www.santafe.edu/research/results/working-papers/acti...

[1] Web search results are underwhelming, I refer you instead to Douglas Montgomery's Introduction to Statistical Process Control.

[aidenn0]

My wife's Kia downshifts when you go more than a certain amount over the set-speed, which lets it apply "negative" throttle with engine braking.