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Specifying Alternative Cost Function with Off-Diagonal Weight Matrices

This example shows how to use non-diagonal weight matrices in a model predictive controller.

Define Plant Model and MPC Controller

The linear plant model has two inputs and two outputs.

plant = ss(tf({1,1;1,2},{[1 .5 1],[.7 .5 1];[1 .4 2],[1 2]}));
[A,B,C,D] = ssdata(plant);
Ts = 0.1;               % sampling time
plant = c2d(plant,Ts);  % convert to discrete time

Create MPC controller.

p=20;       % prediction horizon
m=2;        % control horizon
mpcobj = mpc(plant,Ts,p,m);
-->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.

Define constraints on the manipulated variable.

mpcobj.MV = struct('Min',{-3;-2},'Max',{3;2},'RateMin',{-100;-100},'RateMax',{100;100});

Define non-diagonal output weight. Note that it is specified inside a cell array.

OW = [1 -1]'*[1 -1];
% Non-diagonal output weight, corresponding to ((y1-r1)-(y2-r2))^2
mpcobj.Weights.OutputVariables = {OW};
% Non-diagonal input weight, corresponding to (u1-u2)^2
mpcobj.Weights.ManipulatedVariables = {0.5*OW};

Simulate Using SIM Command

Specify simulation options.

Tstop = 30;               % simulation time
Tf = round(Tstop/Ts);     % number of simulation steps
r = ones(Tf,1)*[1 2];     % reference trajectory

Run the closed-loop simulation and plot results.

[y,t,u] = sim(mpcobj,Tf,r);
-->Assuming output disturbance added to measured output channel #1 is integrated white noise.
-->Assuming output disturbance added to measured output channel #2 is integrated white noise.
-->The "Model.Noise" property of the "mpc" object is empty. Assuming white noise on each measured output channel.

Simulate Using Simulink®

To run this example, Simulink® is required.

if ~mpcchecktoolboxinstalled('simulink')
    disp('Simulink(R) is required to run this part of the example.')

Now simulate closed-loop MPC in Simulink®.

mdl = 'mpc_weightsdemo';

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