Model Predictive Control Toolbox 3.1.1

MPC with Variable Input and Output Bounds

Contents

This demonstration shows how to vary input and output saturation limits in real-time using Model Predictive Control Toolbox™.

Note: Possible limits specified in the MPC object are overridden by limit values fed to the MPC block (if left unconnected, the corresponding limit is considered unbounded).

MPC Controller Setup

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

Plant to be controlled and model used by MPC

plant=tf(1,[1 .8 3]);

Define prediction and control horizons, sampling time, weights, input bounds

p=10;       % prediction horizon (take default one)
m=4;        % control horizon
Ts=.1;      % controller's sampling time

Set weights

clear weights
weights.input=0;
weights.output=1;
weights.inputrate=0.5;

Set constraints

clear mvconstraints
mvconstraints.RateMin = -10;
mvconstraints.RateMax = inf;

Create mpc object

mpc1=mpc(plant,Ts,p,m,weights,mvconstraints);

Simulation Setup

ref=1; % Output set-point
Tsim=20;   %Simulation time
% Open Simulink(R) Model
open_system('mpc_varbounds');
% Turn off input/output not connected warnings since they are irrelevant.
set_param('mpc_varbounds','UnconnectedInputMsg','off')
set_param('mpc_varbounds','UnconnectedOutputMsg','off')
% Start Simulation
sim('mpc_varbounds');

-->Converting the "Model.Plant" property of "mpc" object to state-space.
-->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 whit
e noise on each measured output channel.
-->Converting the "Model.Plant" property of "mpc" object to state-space.
-->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 whit
e noise on each measured output channel.

bdclose('mpc_varbounds');