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.
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