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Using Identified Models

Model Predictive Control Toolbox™ software is able to handle plant models generated by System Identification Toolbox™ software from input/output measurements.

Model Predictive Control Toolbox software labels control input signals as 'Manipulated', measured input disturbances as 'Measured', and unmeasured input disturbances as 'Unmeasured'. On the other hand, System Identification Toolbox software has a different naming rule, as it calls 'Measured' the inputs that are measurable quantities, and 'Noise' those that are not.

When you specify an identified model in the Model Predictive Control constructor as the plant model, Model Predictive Control Toolbox software treats 'Noise' signals as `Unmeasured' input signals, and 'Measured' signals as 'Manipulated' signals, assuming that all measured inputs are also manipulated variables. You can later change later signal types, for instance to specify that some measured inputs are measured disturbances, rather than manipulated variables (see setname).

Model Predictive Control Toolbox software internally converts the identified model you have provided as a plant model into the classical (A,B,C,D) state-space format. The columns of the B matrix originally related to 'Noise' channels are treated as the effect of unmeasured input disturbances on the state of the plant. On the other hand, the columns of the D matrix related to 'Noise' channels as treated as the effect of measurement noise superimposed on the output signal. Accordingly, Model Predictive Control Toolbox software treats as the plant model the state-space model obtained from (A,B,C,D) by zeroing the columns of D related to 'Noise' channels. Those columns are instead used as a static noise model, or cascaded to an existing noise model if you have specified one. A unit static gain is assumed as the disturbance model, unless you have specified another one.


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