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

To view the structure of the model predictive controller, type controller at the MATLAB® command prompt.

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