Specifications

Design Controller Using Identified Model with Noise Channel
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Design Controller Using Identified Model with Noise Channel
This example shows how to design a model predictive controller using an identified plant
model with a nontrivial noise component.
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.
Design a model predictive controller for the identified plant model.
controller = mpc(plant_idss,Ts);
controller is an mpc object. The software treats:
• The measured input of plant_ss as the manipulated variable of controller
• The unmeasured noise input of plant_ss as the unmeasured disturbance of the plant
for controller
• The output of plant_ss as the measured output of the plant for controller
To view the structure of the model predictive controller, at the MATLAB command
prompt, type controller.
You can change the treatment of a plant input in one of two ways:
• Programmatic — Use the setmpcsignals command to modify the signal .
• Model Predictive Control Toolbox design tool — Use the Input signal properties
table to modify the plant model signal types.
You can also design a model predictive controller using:
plant_ss = ss(plant_idss,'augmented');
controller = mpc(plant_ss,Ts);