Control theory is better if you know what you’re doing. ML is technical debt for sure.
The "if you know what you’re doing" here does not refer to the ability to understand control theory. It means that if you know the underlying dynamics, there is mathematically nothing better than controlling those dynamics. Flying a plane, oscillating a circuit, etc. are all things we can do very well without ML because we have exact models of the physical phenomena. Playing chess has no dynamics, control theory is useless. Anything where the dynamics are not "nice" differential equations, ML is probably easier at learning the dynamics than coming up with an ansatz.
There is a surprising amount of structure imposed by the assumption that the dynamics are differential equations, even if you don't know what the differential equations look like. As a consequence, adaptive control laws generally converge a lot faster (like, orders of magnitude faster) than MDP-based RL approaches on the same system being controlled.
The other advantage is that you can prove stability and in some cases have an idea of your performance margin with control theory. THis is important if you eg want your system to receive any sort of accreditation or if you want to fit it into the systems engineering of a more complex system. There's a reason autopilots don't use RL, and it isn't that RL can't be made to work. It's that you can't rigorously prove how robust the RL policy is to changes in the airplane dynamics.
