Model Predictive Control Toolbox 3.1.1

MPC Control with Input Targets

Contents

This demonstration shows how to design an MPC controller to control a system with two inputs and one output, and how to assign a target set-point for the first manipulated variable.

Define the Plant to be Controlled

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

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

Setup an MPC Object

Ts=.4;                      % Sampling time
model=c2d(ss(A,B,C,D),Ts);  % discrete-time prediction model
mpcobj=mpc(model,Ts,20,5);

-->The "Weights.ManipulatedVariables" property of "mpc" object is empty. 
Assuming default 0.00000.
-->The "Weights.ManipulatedVariablesRate" property of "mpc" object is emp
ty. Assuming default 0.10000.
-->The "Weights.OutputVariables" property of "mpc" object is empty. Assum
ing default 1.00000.

Change default weights

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

Input specs

clear MV
MV(1)=struct('RateMin',-.5,'RateMax',.5);
MV(2)=struct('RateMin',-.5,'RateMax',.5);

The following sets up a target set-point u=2 for the first manipulated variable

MV(1).Target=2; % Input steady-state set-point
mpcobj.MV=MV;

Simulation using Simulink®

Tstop=40;                       % Simulation time
open_system('mpc_utarget')      % Open Simulink(R) Model
sim('mpc_utarget',Tstop);       % Start Simulation

-->Integrated white noise added on measured output channel #1.
-->The "Model.Noise" property of the "mpc" object is empty. Assuming whit
e noise on each measured output channel.

bdclose('mpc_utarget')