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Setting Targets for Manipulated Variables

This example shows how to design a model predictive controller for a plant with two inputs and one output with target set-point for a manipulated variable.

Define Plant Model

The linear plant model has two inputs and two outputs.

N1 = [3 1];
D1 = [1 2*.3 1];
N2 = [2 1];
D2 = [1 2*.5 1];
plant = ss(tf({N1,N2},{D1,D2}));
A = plant.a;
B = plant.b;
C = plant.c;
D = plant.d;
x0 = [0 0 0 0]';

Design MPC Controller

Create MPC controller.

Ts = 0.4;                      % Sampling time
mpcobj = mpc(plant,Ts,20,5);
-->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.

Specify weights.

mpcobj.weights.manipulated = [0.3 0]; % weight difference MV#1 - Target#1
mpcobj.weights.manipulatedrate = [0 0];
mpcobj.weights.output = 1;

Define input specifications.

mpcobj.MV = struct('RateMin',{-0.5;-0.5},'RateMax',{0.5;0.5});

Specify target set-point u=2 for the first manipulated variable.

mpcobj.MV(1).Target=2;

Simulation Using Simulink®

To run this example, Simulink® is required.

if ~mpcchecktoolboxinstalled('simulink')
    disp('Simulink(R) is required to run this example.')
    return
end

Simulate.

mdl = 'mpc_utarget';
open_system(mdl)      % Open Simulink(R) Model
sim(mdl);             % Start Simulation
-->Converting model to discrete time.
-->Integrated white noise added on measured output channel #1.
-->The "Model.Noise" property of the "mpc" object is empty. Assuming white noise on each measured output channel.

bdclose(mdl)
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