Feedforward control is unusual. In feedback control, you look at the error and try to zero it out. Usually, there's some lag in the system, which leads to undershoot, overshoot, oscillation, and all the usual problems of feedback systems. Maxwell (yes, that Maxwell) figured all this out in 1868, and his little paper "On Governors" defined linear control theory for a century.[1]
Feedforward control, though, is about measuring disturbance inputs, predicting what's going to happen, and adjusting controllable inputs so that the error remains near zero. An example is a heating system with an input for outside temperature as well as inside temperature. When the outside gets cold, the heating plant will be cranked up in advance of the inside getting cold. This is essential if the system being controlled has a lot of lag, like a big building's heating system.
Adaptive feedforward control is automatically identifying how the inputs affect the outputs by watching everything for a while. It's thus a form of machine learning. For the heating system example, a good controller might learn that a 10 degree drop outside means a 5 degree drop inside 1 hour later. It might also learn that the heating hot water temperature rises at 30 degrees an hour when the boiler is at full power. Such info can be used to decide to crank up the boiler half an hour after the outside temperature drops, so heating water will be available just when it's needed. This is a basic form of machine learning.
One big difference from the machine learning community is that controls people demand error bounds on their control algorithms. So the machine learning techniques used tend to be the ones ameninable to error analysis. Support vector machines, yes; deep neural nets, not so much.
[1] https://commons.wikimedia.org/wiki/File%3AOn_Governors.pdf
Convolution integral, Laplace transform, Z transform, frequency domain, impulse response, step response, digital filters... it's all amazing.