[From Rick Marken (950920.1030)]

Hans Blom (950920) says that his model-based control system:

is NOT a "control of perception" model.

In fact, it is. I finally figured out why Hans' model based controller works.

It works because it is essentially the same as the PCT model that Bill Powers

posted. The only difference is that Hans' model adds a "noise" term to the

error signal; this noise term improves control (relative to the basic control

model) if 1) the disturbance is relatively smooth and 2) the feedback

function is known (or can be estimated).

The easiest way to see this is to look at the code for the two models with

the reference signal, r[ ], constant at 0 and the environmental coefficient,

b, constant at 1. In that case, the basic control model is:

u := u - x

where u is the output variable and x is the perceptual variable.

Hans' model based control model is:

dnew := x - u;

dpre := 2.0 * dnew - dold;

u := - dpre;

In these equations, dnew is viewed as an estimate of the current disturbance,

dpre the "predicted" value of the disturbance at the next time instant and

dold is the previous disturbance estimate. This way of looking at the

variables in Hans' model obscures the fact that the code segment above is

equivalent to:

u := u - x + (u - x + dold)

Looked at in this way, we can see that the output of Hans' model (u) is

EXACTLY the same as the output of the control model (u := u-x) with the

addition of an extra quantity (u - x + dold) that I call the "noise" term.

This noise term is added by the algorithm that "predicts" the disturbance

values. It is "noise" in the sense that it is an independent addition to the

error signal (0-x) that drives the integrated output (u). If the actual

disturbance is smooth over time, this noise term improves control by adding

what usually turns out to be an appropriate amount to the output integral; if

the disturbance is not smooth (if, for example, it is a square wave), this

noise term degrades control (relative to that produced by the basic PCT

model).

So Hans' model is fundamentally the same as the PCT model; it controls a

perceptual variable, x, keeping it close to the reference signal value

despite disturbances. Hans' model extends the basic PCT model by trying to

add "intelligent noise" to the error signal; noise that will improve control.

This intelligent noise does improve control under many circumstances.

Although Hans' model based addition to the PCT model can improve control

under certain circumstances, there is no evidence that this addition is

needed to explain anything about the behavior of living control systems. It

MAY eventually prove to be an important addition to the basic PCT model, but

as yet, there is no data I know of that demands such an addition to the model.

Hans' model-based control model does not, in any way, contradict the basic

insight of PCT (that behavior is the control of percpmion); in fact, Hans

model can be used to illustrate the basic point of PCT, viz. when a system

(living or artifactual) controls, what it controls is a perceptual

representation of a variable or variables in its environment. The PCT model

controls a perceptual variable (x in the model code); Hans' model controls

the same perceptual variable. Hans' model controls better than the PCT model

in many circumstances; but what Hans model controls is what all control

systems control: perception.

Best

Rick