Документация

sys = ss(tf({1,1,1},{[1 .5 1],[1 1],[.7 .5 1]}));
Ts = 0.2;
model = c2d(sys,Ts);

model = setmpcsignals(model,'MV',1,'MD',2,'UD',3);

mpcobj = mpc(model,Ts,10,3);

mpcobj.MV = struct('Min',0,'Max',1,'RateMin',-10,'RateMax',10);

mpcobj.Model.Disturbance = tf(sqrt(1000),[1 0]);

Tstop = 30;                               % simulation time
Tf = round(Tstop/Ts);                     % number of simulation steps
r = ones(Tf,1);                           % reference signal
v = [zeros(Tf/3,1);ones(2*Tf/3,1)];       % measured disturbance signal

sim(mpcobj,Tf,r,v)

SimOptions = mpcsimopt(mpcobj);
d = [zeros(2*Tf/3,1);-0.5*ones(Tf/3,1)];
SimOptions.Unmeas = d;                          % unmeasured input disturbance signal
SimOptions.OutputNoise=.001*(rand(Tf,1)-.5);    % output measurement noise
SimOptions.InputNoise=.05*(rand(Tf,1)-.5);      % noise on manipulated variables

[y,t,u,xp] = sim(mpcobj,Tf,r,v,SimOptions);

figure
subplot(2,1,1)
plot(0:Tf-1,y,0:Tf-1,r)
title('Output')
grid
subplot(2,1,2)
plot(0:Tf-1,u)
title('Input')
grid

simModel = ss(tf({1,1,1},{[1 .8 1],[1 2],[.6 .6 1]}));
simModel = setmpcsignals(simModel,'MV',1,'MD',2,'UD',3);
simModel = struct('Plant',simModel);
simModel.Nominal.Y = 0.1;
simModel.Nominal.X = -.1*[1 1 1 1 1];

SimOptions.Model = simModel;
SimOptions.plantinit = [0.1 0 -0.1 0 .05];  % Initial state of the true plant
SimOptions.OutputNoise = [];                % remove output measurement noise
SimOptions.InputNoise = [];                 % remove noise on manipulated variables

% Run the closed-loop simulation and plot results.
sim(mpcobj,Tf,r,v,SimOptions)

mpcobj.MV.MinECR = 1;
mpcobj.MV.MaxECR = 1;

mpcobj.OV.Max = 1.1;

sim(mpcobj,Tf,r,v)

mpcobj.OV.MaxECR = 0.001;  % The closer to zero, the harder the constraint
sim(mpcobj,Tf,r,v)

[L,M,A1,Cm1] = getEstimator(mpcobj);

e = eig(A1-A1*M*Cm1)

poles = [.8 .75 .7 .85 .6 .81];
L = place(A1',Cm1',poles)';
M = A1\L;
setEstimator(mpcobj,L,M);

SimOptions.OpenLoop = 'on';
SimOptions.MVSignal = sin((0:Tf-1)'/10);

sim(mpcobj,Tf,[],v,SimOptions)

DC = cloffset(mpcobj);
fprintf('DC gain from output disturbance to output = %5.8f (=%g) \n',DC,DC);

[A,B,C,D] = ssdata(model);
Tstop = 5;                  % Simulation time
x = [0 0 0 0 0]';           % Initial state of the plant
xmpc = mpcstate(mpcobj);    % Initial state of the MPC controller
r = 1;                      % Output reference signal

YY=[];
UU=[];
XX=[];

for t=0:round(Tstop/Ts)-1
    % Store states
    XX = [XX,x]; %#ok<*AGROW>
    % Define measured disturbance signal
    v = 0;
    if t*Ts>=10
        v = 1;
    end
    % Define unmeasured disturbance signal
    d = 0;
    if t*Ts>=20
       d = -0.5;
    end
    % Plant equations: output update (no feedthrough from MV to Y)
    y = C*x + D(:,2)*v + D(:,3)*d;
    YY = [YY,y];
    % Compute MPC action
    u = mpcmove(mpcobj,xmpc,y,r,v);
    % Plant equations: state update
    x = A*x + B(:,1)*u + B(:,2)*v + B(:,3)*d;
    UU = [UU,u];
end

figure
subplot(2,1,1)
plot(0:Ts:Tstop-Ts,YY)
grid
title('Output')
subplot(2,1,2)
plot(0:Ts:Tstop-Ts,UU)
grid
title('Input')

r = 0.5;
v = 0;
[~,Info] = mpcmove(mpcobj,xmpc,y,r,v);

topt = Info.Topt;
yopt = Info.Yopt;
uopt = Info.Uopt;
figure
subplot(2,1,1)
stairs(topt,yopt)
title('Optimal sequence of predicted outputs')
grid
subplot(2,1,2)
stairs(topt,uopt)
title('Optimal sequence of manipulated variables')
grid

LTI = ss(mpcobj,'rv');

[AL,BL,CL,DL] = ssdata(LTI);

mpcobj.MV = [];           % No input constraints
mpcobj.OV = [];           % No output constraints
Tstop = 5;                %Simulation time
xL = zeros(size(BL,1),1); % Initial state of linearized MPC controller
x = [0 0 0 0 0]';         % Initial state of plant
y = 0;                    % Initial measured output
r = 1;                    % Output reference set-point
u = 0;                    % Previous input command
YY = [];
XX = [];
xmpc = mpcstate(mpcobj);
for t = 0:round(Tstop/Ts)-1
    YY = [YY,y];
    XX = [XX,x];
    v = 0;
    if t*Ts>=10
        v = 1;
    end
    d = 0;
    if t*Ts>=20
        d = -0.5;
    end
    uold = u;
    % Compute the linear MPC control action
    u = CL*xL+DL*[y;r;v];
    % Compare the input move with the one provided by MPCMOVE
    uMPC = mpcmove(mpcobj,xmpc,y,r,v);
    dispStr(t+1) = {sprintf('t=%5.2f, u=%7.4f (provided by LTI), u=%7.4f (provided by MPCOBJ)',t*Ts,u,uMPC)}; %#ok<*SAGROW>
    % Update plant equations
    x = A*x + B(:,1)*u + B(:,2)*v + B(:,3)*d;
    % Update controller equations
    xL = AL*xL + BL*[y;r;v];
    % Update output equations
    y = C*x + D(:,1)*u + D(:,2)*v + D(:,3)*d;
end

for t=0:round(Tstop/Ts)-1
    disp(dispStr{t+1});
end

figure
plot(0:Ts:Tstop-Ts,YY)
grid

SimOptions = mpcsimopt(mpcobj);
SimOptions.Constr = 'off';    % Remove all MPC constraints
SimOptions.Unmeas = d;        % unmeasured input disturbance
sim(mpcobj,Tf,r,v,SimOptions);

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

mpcobj = mpc(model,Ts,10,3);
mpcobj.MV = struct('Min',0,'Max',1,'RateMin',-10,'RateMax',10);
mpcobj.Model.Disturbance = tf(sqrt(1000),[1 0]);

[A,B,C,D] = ssdata(sys);

mdl1 = 'mpc_miso';
open_system(mdl1)
sim(mdl1)

omega = 2*pi/10;
mpcobj.Model.Noise = 0.5*tf(omega^2,[1 0 omega^2]);

mpcobj.Model.Disturbance = tf(0.1);
mpcobj.weights = struct('MV',0,'MVRate',0.1,'OV',0.005);
mpcobj.predictionhorizon = 40;
mpcobj.controlhorizon = 3;

Tstop = 150;
mdl2 = 'mpc_misonoise';
open_system(mdl2)
sim(mdl2,Tstop)

bdclose(mdl1)
bdclose(mdl2)