Are you collectively referring to the introductory papers ?

No, the "book of readings", although there appears to be a huge overlap between the book and the papers you linked to.

One of the incredibly unfortunate things about that website is that it spends way too much time talking about what you'll learn once you understand PCT, compared to how much time it spends actually explaining PCT. OTOH, my guess is that most of the people whose writing is quoted there have already read the key book (Behavior: The Control Of Perception), and find it hard to explain the concepts in a much shorter form. However, there are a few good chapters and a lot of small insights in the other chapters of the "book of readings", so that by the end I was at least convinced enough to plunk down 35 bucks for the 2nd edition of B:CP (as it's usually abbreviated).

For my purposes, it really didn't hurt that my mind was already on ideas rather similar to reference levels, based on some recent change experiences (not to mention some discussions here), so I was quite motivated to dig through the testimonials to find some actual meat.

One of the other really useful bits on the site are Powers' 1979 robotics articles for Byte magazine, which I didn't find before I bought the book, and which might be an adequate substitute for some portion of the book, if you read all four of them.

One insightful tidbit from the second article:

We have now established the fact that using natural logic and following causes and effects around the closed loop as a sequence of events will lead to a wrong prediction of control system behavior. This immediately eliminates three-quarters of what biologists, psychologists, neurologists, and even cyberneticians have published about control theory and behavior. We are just beginning to see that one must view all the variables in a control system as changing together, not one at a time. This is what I mean by retraining the intuition. Cartesian concepts of cause and effect, and Newtonian physics, have trained us to think along directed lines. What we need to do to understand control systems is to learn how to think in circles

The above came just after he painstakingly goes through the discrete math (using BASIC code) to show why the intuitive math for a certain control system is wrong, in that it leads to an unstable system... whereas the simpler, PCT-based approach results in more robust behavior. Another tidbit:

You will notice that doubling the error sensitivity, which doubles the amount of output generated by a given error, does not double the amount of output that actually occurs. Far from it. When, for any reason, the loop gain goes up, the steady state error simply gets smaller, assuming that the system remains stable. This fact does violence to the popular idea that the brain commands muscles to produce behavior. If that were the case, doubling the sensitivity of a muscle to the nerve signals reaching it ought to produce twice as much muscle tension. Nothing of the sort happens, unless you’ve lopped off the rest of the nervous system, particularly the feedback paths.

Basically (no pun intended), the articles describe a series of models and simulations (written in BASIC) that demonstrate the basic principles and models of behavior being generated by hierarchies of negative-feedback control, where the output of a "higher" control layer determines the reference values for the "lower" layers, and why this is a far more parsimonious and robust model of what living creatures appear to be doing, than more traditional models.