Specifications

Design Controller for Identified Plant
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Design Controller for Identified Plant
This example shows how to design a model predictive controller for a linear identified
plant model.
This example uses only the measured input and output of the identified model for the
model predictive controller plant model.
This example requires a System Identification Toolbox license.
Obtain an identified linear plant model.
load dryer2;
Ts = 0.08;
dry_data = iddata(y2,u2,Ts);
dry_data_detrended = detrend(dry_data);
plant_idss = ssest(dry_data_detrended,3);
plant_idss is a third-order, identified state-space model that contains one measured
input and one unmeasured (noise) input.
Convert the identified plant model to a numeric LTI model.
You can convert the identified model to an ss, tf, or zpk model. For this example,
convert the identified state-space model to a numeric state-space model.
plant_ss = ss(plant_idss);
plant_ss contains the measured input and output of plant_idss. The software
discards the noise input of plant_idss when it creates plant_ss.
Design a model predictive controller for the numeric plant model.
controller = mpc(plant_ss,Ts);
controller is an mpc object. The software treats:
• The measured input of plant_ss as the manipulated variable of controller
• The output of plant_ss as the measured output of the plant for controller