All control systems DO have models of what they are controlling. However, the models are typically VERY simple.
A good principle for constructing control systems are: Given that I have a very simple model, how do I optimize it?
The models I learned about in cybernetics were all linear, implemented as matrices, resistors and capacitors, or discrete time step filters. The most important thing was to show that the models and reality together did not result in amplification of oscillations. Then one made sure that the system actually did some controlling, and then one could fine tune it to reality to make it faster, more stable, etc.
One big advantage of linear models is that they can be inverted, and eigenvectors found. Doing equivalent stuff for other kinds of models is often very difficult, requiring lots of computation, or is simply impossible.
As has someone has written before here: It is mathematically justified to consider linear control systems as having statistical models of reality, typically involving gaussian distributions.
Kim Øyhus
Verbal probabilities are typically impossible because the priors are unknown and important.
However: relative probabilities and similar can often be given usueful estimates, or limits.
For instance: Seeing a cat is more likely than seeing a black cat because black cats are a subset of cats.
Stuff like this is the reason that pure probability calculations are not sufficient for general intelligence.
Probability distributions however, seem to me to be sufficient. This cat example cuts the distribution in 2.
Kim Øyhus