It pretty much is the case.
Part of it is that predictability is a desirable feature in these systems, but also that problems like the one described in the article you don't really need things like ML. The majority of control problems like this are surprisingly straightforward. They might be complex, in the sense of having a lot of variables, but the physics involved is well understood and can be modelled using traditional techniques.
The progress of self-driving cars is a good example of this. I can remember seeing expeimental self driving cars many years ago, but always going round mostly empty test tracks. Driving a car isn't that difficult for a computer system, what's hard is driving in highly complex urban environments with many other cars around that you need to predict.
Planes, in contrast, have a rather simple environment. The number of objects they have to avoid is massively lower, and their freedom of movement is higher, with established rules for how to behave, there are no traffic signs to interpret. This means that all you are really doing is object detection with radar, and collision avoidance.
In addition, modern combat planes are effectively flown by a computer all the time anyway, with the pilot providing the instructions. A number of fighter planes, especially the most modern, are essentially unflyable without computers due to their aerodynamics. Most are inherently unstable around at least one axis, which makes them more manouverable, but means they will not fly stably in the way a 747 will.