One approach is that you have a semi-traditional control system, which has integrators, filters, adders, etc. All these components have tuning parameters, and tuning complex controllers is hard. But once you get them tuned, they behave in a reasonably predictable way.

The machine learning part is used to get a predictor. You operate the thing and train a model of what will happen for various inputs. The machine learning system is just an observer in this training phase; it doesn't control anything. You train from recorded data.

Then you use the trained model to tune the controller. The model lets you get an output for any set of inputs, so you can now choose input test sets which are suitable for tuning the model, like changing one input at a time and noting the output change.

Automatically generating the structure of the controller (how those blocks are connected) is a separate problem. That's called "system identification".

See [1].

[1] http://www.mpc.berkeley.edu/research/adaptive-and-learning-p...