This is machine translation

Translated by Microsoft
Mouseover text to see original. Click the button below to return to the English verison of the page.

Note: This page has been translated by MathWorks. Please click here
To view all translated materals including this page, select Japan from the country navigator on the bottom of this page.

Using Custom State Estimation

This example shows how to use measurable plant states in MPC control at run time.

Define Plant Model

The linear open-loop plant model is a double integrator.

plant = tf(1,[1 0 0]);

Design MPC Controller

Create the controller object with sampling period, prediction and control horizons.

Ts = 0.1;
p = 10;
m = 3;
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.

Specify actuator saturation limits as MV constraints.

mpcobj.MV = struct('Min',-1,'Max',1);

Specify the controller to use custom state estimation.


Simulate Using MPCMOVE Command

Configure variables to store the closed-loop responses.

Tf = round(5/Ts);
YY = zeros(Tf,1);
UU = zeros(Tf,1);

Prepare the plant used in simulation.

sys = c2d(ss(plant),Ts);
xsys = [0;0];

Use MPCSTATE object to specify the initial controller states before simulation starts.

xmpc = mpcstate(mpcobj);
-->Converting the "Model.Plant" property of "mpc" object to state-space.
-->Converting model to discrete time.
   Assuming no disturbance added to measured output channel #1.
-->The "Model.Noise" property of the "mpc" object is empty. Assuming white noise on each measured output channel.

Simulate closed-loop response iteratively.

for t = 0:Tf
    % Update plant measurement.
    ysys = sys.C*xsys;
    % Compute MPC action. Because the plant states are measurable, use
    % these values for state estimation instead of the plant output.
    xmpc.Plant = xsys;
    u = mpcmove(mpcobj,xmpc,[],1);
    % Store signals.
    YY(t+1) = ysys;
    UU(t+1) = u;
    % Update plant states.
    xsys = sys.A*xsys + sys.B*u;

Simulate Using Simulink®

To run this example, Simulink® is required.

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

Simulate closed-loop control of the linear plant model in Simulink. Controller "mpcobj" is specified in the MPC Controller block dialog.

mdl = 'mpc_customestimation';

The closed-loop responses are identical between simulations in MATLAB and Simulink.

fprintf('\nDifference between simulations in MATLAB and Simulink is %g\n',norm(UU-u));
Difference between simulations in MATLAB and Simulink is 5.76995e-14
Was this topic helpful?