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Design Controller for Identified Plant

This example shows how to design a model predictive controller using an identified plant model. The internal plant model of the controller uses only the measured input and output of the identified model.

This example requires a System Identification Toolbox™ license.

Load the input/output data for identification.

load dryer2
Ts = 0.08;

Create an iddata object from the input

dry_data = iddata(y2,u2,Ts);
dry_data_detrended = detrend(dry_data);

Estimate a linear state-space plant model.

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 component.

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 component of plant_idss when it creates plant_ss.

Design a model predictive controller for the numeric plant model.

controller = mpc(plant_ss,Ts);
-->The "PredictionHorizon" property of "mpc" object is empty. Trying PredictionHorizon = 10.
-->The "ControlHorizon" property of the "mpc" object is empty. Assuming 2.
-->The "Weights.ManipulatedVariables" property of "mpc" object is empty. Assuming default 0.00000.
-->The "Weights.ManipulatedVariablesRate" property of "mpc" object is empty. Assuming default 0.10000.
-->The "Weights.OutputVariables" property of "mpc" object is empty. Assuming default 1.00000.

controller is an mpc object in which:

  • The measured input of plant_ss is a manipulated variable.

  • The output of plant_ss is a measured output.

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

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