Any good refs on Model-Based-Control? (is it really as good as is claimed?)

With a good system, model and controller, I don't see why that kind of
precision isn't possible.

There are a number of model based control systems, including direct matrix
control, dynamic matrix control, smith controllers, perfect controllers,
etc.  Any good modern control book should have a pretty good explanation of
how to implement such a control.

The basic idea is that you figure out exactly what needs to be done, based
on the model of the system, then do it to the system.  As long as your
system behaves as the model says it does, things work great.  If you have
some deviation, then the control can degrade somewhat.

Typically, the models are constructed "backwards" from the system, so that
you put in what you want to do, and it tells you how to do it based on
current conditions.  The system then takes what you do to it and gives back
new conditions (which hopefully are the ones you are looking for).  For
example, if you wanted to change the temperature from 0C to 1000 C, the
model would take into account what sort of heating rate can be applied, any
sort of losses in the system, and perhaps a few other constraints, then
figure out how many watts to apply for how many seconds to achieve the
temperature.  It would then determine how much the thermal losses were, and
call for that amount of heat to be added at the appropriate moment.   A
little feedback would let the model know where it was in the process and
possibly allow a little fine tuning.

Even PID controlls can be tuned quite tightly, though it would take a very
good system with a fantastic polling rate to have the precision required for
what you mentioned.

Michael

Perfb@yahoo.com wrote:

1) All Control System Design derives from a model of the system to be controlled.

2) Some approaches to the design and of a feedback controller make use of a
model - whether explicit
or implicit, within the implementation of a controller. This is what is
typically meant by
'Model-based Control' - which is essentially a lay-person's terminology - not
having a precise
definition within the discipline of Control Engineering.

3) One approach to controller design that incorporates an explicit model of the
controlled system is
'Model-Predictive Control' (MPC). Commercial implementations of MPC exist under
various trade names,
such as 'Dynamic Matrix Control' in the field of Process Control. Sometimes
people who are not
Control Engineers use the term 'Model-based Control' to mean specifically MPC.

4) There exists a Model-Predictive Control Toolbox for MATLAB.

5) Much information about MPC, issues of robustness etc., are to be found in the
book by Manfred
Morari, which is an accompaniment to the MATLAB Toolbox.
<http://www.amazon.co.uk/exec/obidos/ASIN/0135905060/khaceconsultinge>

I hope this helps.

Kelvin B. Hales
Kelvin Hales Associates Limited
Consulting Control Engineers
Web: www.khace.com

Thanks very much for the link!  I am looking at the MPC Matlab toolbox
now.

Does anyone know what it would take to actually implement such a
control on a typical DSP eg TI F2812?  eg is a code generator package
eg VisSIM really mandatory or can one implement it with a generic
matrix library?  assuming real-world update rates of ~10Hz and 2-5
inputs/outputs and ~30second first-order system time constants.

Hello,


applying an MPC controller in practice requires to solve a given
optimization problem on-line for the current value of the state.
Depending on dimensions of your system and lenght of the prediction
horizon, it might or might not be feasible to solve the problem within a
given sampling period. If the physical limitations do not allow you to
use the on-line implementation of MPC, you can use the so-called
explicit MPC for your problem.

The aim of explicit solution to MPC problems is to obtain an explicit
representation of the state feedback law for _all_ states which satisfy
system constraints. The resulting solution takes a form of a
piecewise-affine state feedback which is defined over some polyhedral
partition of the state space. Implementation of such controllers boils
down to a simple set-membership test which can be performed easily on
your DSP. FYI, we have applications with sampling rates in order of
kiloherz, or even faster.

There is a free (GLPed) Matlab toolbox available to solve these kind of
problems, called Multi-Parametric Toolbox:

http://control.ee.ethz.ch/~mpt/

It solves both on-line and explicit MPC problems, i.e. you can use it as
a free substitue of the MPC toolbox from Mathworks. MPC controllers can
be constructed for linear and hybrid systems (systems involving
switches, binary and integer variables, etc.) with constraints.

The toolbox allows to export the resulting explicit control law to
C-code which can be directly transfered to your DSP. Alternativelly you
can use Simulink and RTW to download such controllers to any supported
target machine.

Best,
Michal

--
------------------------------------------------------------------
Michal Kvasnica              | Phone :+41 1 632 4274
Automatic Control Laboratory | Fax :+41 1 632 1211
ETL K12, ETH Zentrum         | mailto:kvasnica@control.ee.ethz.ch
8092 Zurich, Switzerland     | http://control.ee.ethz.ch/~kvasnica

perfb@yahoo.com wrote:

I did something very much like that using a Phelan-type control system
(http://users.rcn.com/jyavins/servo.html ) running on a 20 MHz 80186.
Repeatability to .1C is feasible, but that degree of absolute accuracy
is questionable. Such specs are often put forth with the attitude, "I'll
retract it if you can prove me wrong."

Jerry
--
Engineering is the art of making what you want from things you can get.
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