Specifications

2 Building Models
2-14
Input and Output Types
General Case
As mentioned in “Signal Types” on page 2-8, Model Predictive Control Toolbox software
supports three input types and two output types. In a Model Predictive Control Toolbox
design, designation of the input and output types determines the controller dimensions
and has other important consequences.
For example, suppose your plant structure were as follows:
Plant Inputs Plant Outputs
Two manipulated variables (MVs) Three measured outputs (MOs)
One measured disturbance (MD) Two unmeasured outputs (UOs)
Two unmeasured disturbances (UDs) 
The resulting controller has four inputs (the three MOs and the MD) and two outputs
(the MVs). It includes feedforward compensation for the measured disturbance, and
assumes that you wanted to include the unmeasured disturbances and outputs as part of
the regulator design.
If you didn't want a particular signal to be treated as one of the above types, you could do
one of the following:
• Eliminate the signal before using the model in controller design.
• For an output, designate it as unmeasured, then set its weight to zero (see “Output
Weights”).
• For an input, designate it as an unmeasured disturbance, then define a custom state
estimator that ignores the input (see “Disturbance Modeling and Estimation”).
Note By default, the toolbox assumes that unspecified plant inputs are manipulated
variables, and unspecified outputs are measured. Thus, if you didn't specify signal
types in the above example, the controller would have four inputs (assuming all
plant outputs were measured) and five outputs (assuming all plant inputs were
manipulated variables).
For model CSTR, default Model Predictive Control Toolbox assumptions are incorrect.
You must set its InputGroup and OutputGroup properties, as illustrated in the above
code, or modify the default settings when you load the model into the design tool.