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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);

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MATLAB MODEL PREDICTIVE CONTROL TOOLBOX - S Spécifications Page 47