mpcverbosity off;          % turn off mpc messaging
plant=tf(1,[1 1],0.2);     % create plant (0.2 seconds sampling time)
mpcobj=mpc(plant,0.2);     % create mpc object (0.2 second sampling time)

ktf=tf(mpcobj)             % convert mpc to transfer function

ktf =
 
  From input "MO1" to output "MV1":
    0.452 z^3 - 0.6781 z^2 - 1.506e-16 z
  -----------------------------------------
  z^3 - 1.001 z^2 + 0.0002642 z + 0.0006399
 
Sample time: 0.2 seconds
Discrete-time transfer function.

% create a plant and the corresponding mpc object
mpcverbosity off;                  % turn off mpc messaging
plant=tf({1,1},{[1 1],[1 1]},0.2); % create plant (0.2 seconds sampling time)
plant=setmpcsignals(plant,'UD',2); % second input is a disturbance entering at mv
mpcobj=mpc(plant,0.2);             % create mpc object (0.2 second sampling time)

% set input and output disturbance models (remove integrators)
setindist(mpcobj,'model',tf(1))    % set input disturbance model to 1
setoutdist(mpcobj,'model',tf(1))   % set output disturbance model to 1

% closed loop output sensitivity
cloffset(mpcobj)
ans =
    1.4472

% convert the controller and calculate closed loop transfer function
ktf=tf(mpcobj);                    % convert mpc to transfer function
Muy=feedback(plant(:,1),ktf,1);    % closed loop transfer function from mv to y

% closed loop output sensitivity using transfer function
1+dcgain(Muy*ktf)
ans =
    1.4472

% plot closed loop response to a step on the measured input
step(Muy,10);                                     % simulate using step

% create option object to inject disturbance in simulation
SimOptions = mpcsimopt;                           % create object
SimOptions.UnmeasuredDisturbance = ones(50,1);    % specify unmeasured input disturbance

% simulate closed loop for 50 steps with sim and step and plot the response
[y,t,u,xp]=sim(mpcobj,50,0,[],SimOptions);           % simulate using sim

% overlap the sim results on the plot
hold on
stairs(t,y,'r')
hold off