[From Rick Marken (960716.1330)]
Bill Leach (960715.1231)
Model based control is anything but "minor" in engineering design.
I think what you are calling "model based control" is what I would call
"adaptive control" (or reorganization in PCT).
The first controllers that began "wearing the term 'model based' on their
shirtsleaves" were the adaptive controller...These controllers monitor their
own control error and change the parameters (and even the output functions
used) to reduce average and/or peak error based upon operating history.
I agree that this kind of adaptive control is important in engineered AND in
living control systems; it's precisely what we call reorganization. Hans'
model based controller does involve some adaptation; but the essense of model
based "control" is output generation based on the model. This kind of control
of output system will be successful (from the point of view of an observer)
only after adaptation; that is, only after the appropriate "model" has been
We know from many sources that _new_ motor function tasks are controlled
poorly if at all when first attempted.
Yes. But we get better, not because we learn a model of the environment (as
Hans' system does) but because we tune (adapt) the parameters and functions
of our control organization (perceptual functions, output funcitons, gains,
lags, etc) so that we control better. Hans' model based system learns the
differential equations (environmental laws) that relate output to intended
result. Then, when there is no perceeption of the result, the system can
produce that result by simply specifying the appropriate outputs; control of
As Bill Powers noted in an earlier post, when perception is available, the
model allows slightly improved control (relative to the plain vanilla PCT
model) when the time constant of model based prediction is short; but then
there is little gained from the model in terms of the ability to control when
perception is lost. When the time constant of prediction is long, the model
can produce the intended result without perceptual input for a much longer
time than the PCT model; but in this case model based control no better
than the PCT model when perception is present.
So the only thing to be gained from a model-based system like the one Hans is
talking about is the ability to produce a particular result for a relatively
long time (assuming no disturbance variations) when the perceptual
representation of that result is lost. Then the question is, do people
produce intended results for a long period of time when perception of the
result is lost? We might but we have seen no evidence presented of it and if
it does happen it is certainly "non-nominal".. Also, it's not clear that
whatever ability there is to "control" without perception cannot be handled
by the memory (and imagination) model Bill described in B:CP.
As much as Hans seems to be argueing for "generated output" I get the
feeling that he does not really mean it the way that most of us usually take
him to mean.
I have looked at the code for and run Hans' model based controller. It is not
just an adaptive controller, changing the parameters of a perceptual control
system. It is an adaptive controller that is "adapting" a linear equation
that is the system's "model" of the presumed environmental laws relating
output to input. Once the model has learned the environmental equation you
can switch off the perceptual input to the model and the system will generate
outputs that produce the intended result. Hans' model based controller uses
closed loop adaptation to learn a model that lets it act as an open loop
"controller" (I put "controller" in quotes because the system cannot resist
disturbances as long as it is running open loop).
Adaptation and tuning of lower level control loops has been posited as a
form of learning and this process is undoubtedly also a model based control
Again, I think adaptive and model-based control are two different things. I
think you are using "model based" control as a synonym for adaptive control;
I think model-based control always must involve adaptive control (to learn
the model) but not all adaptive control is "model based". Adaptive control is
good old fashioned closed loop control where the controlled variable is a
measure of the performance or another control system -- like error -- and
the adaptive system controls this variable by manipulating paramters of the
control system. Model based control, on the other hand, is aimed at output
generation; the model may be built based on closed loop adaptive processes
but the goal is to learn a model that will allow the production of intended
results "in the blind".