Create Plant Model and MPC Controller

Define a SISO discrete-time plant with sample timeTs.

Ts = 0.1; plant = c2d(tf(1,[1 .8 3]),Ts); [A,B,C,D] = ssdata(plant);

Create an MPC controller with specified prediction horizon,p, control horizon,c, and sample time,Ts. Useplantas the internal prediction model.

p = 10; m = 4; mpcobj = mpc(plant,Ts,p,m);

-->The "Weights.ManipulatedVariables" property is empty. Assuming default 0.00000. -->The "Weights.ManipulatedVariablesRate" property is empty. Assuming default 0.10000. -->The "Weights.OutputVariables" property is empty. Assuming default 1.00000.

Specify controller tuning weights.

mpcobj.Weights.MV = 0; mpcobj.Weights.MVrate = 0.5; mpcobj.Weights.OV = 1;

For this example, the upper and lower bounds on the manipulated variable, and the upper bound on the output variable are varied at run time. To do so, you must first define initial dummy finite values for these constraints in the MPC controller object. Specify values forMV。Min,MV。马克斯, andOV.Max.

At run time, these constraints are changed using anmpcmoveoptobject at the command line or corresponding input signals to the MPC Controller block.

mpcobj.MV.Min = 1; mpcobj.MV.Max = 1; mpcobj.OV.Max = 1;

Simulate Model Using Simulink

Open Simulink Model.

mdl ='mpc_varbounds'; open_system(mdl)

In this model, the input minimum and maximum constraint ports (uminandumax) and the output maximum constraint port (ymax)of the MPC Controller block are enabled. Since the minimum output bound is unconstrained, theymininput port is disabled.

Configure the output setpoint,ref, and simulation duration,Tsim.

ref = 1; Tsim = 20;

Run the simulation, and view the input and output responses in the I/O scope.

sim(mdl) open_system([mdl'/I//O'])

-->Converting the "Model.Plant" property to state-space. -->Assuming output disturbance added to measured output channel #1 is integrated white noise. -->The "Model.Noise" property is empty. Assuming white noise on each measured output.

Simulate Model at Command Line

Specify the initial state of the plant and controller.

x = zeros(size(B,1),1); xmpc = mpcstate(mpcobj);

Store the closed-loop output, manipulated variable, and state trajectories of the MPC controller in arraysYY,UU, andXX, respectively.

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

Create anmpcmoveopt对象specifying the run-time bound values.

options = mpcmoveopt;

Run the simulation loop.

fort = 0:round(Tsim/Ts)% Store the plant state.XX = [XX; x];% Compute and store the plant output. There is no direct feedthrough% from the input to the output.y = C*x; YY = [YY; y'];% Get the reference signal value from the data output by the Simulink% simulation.ref = yout.Data(t+1,2);% Update the input and output bounds. For consistency, use the% constraint values output by the Simulink simulation.options.MVMin = uout.Data(t+1,2); options.MVMax = uout.Data(t+1,3); options.OutputMax = yout.Data(t+1,3);% Compute the MPC control action.u = mpcmove(mpcobj,xmpc,y,ref,[],options);%更新plant state and store the input signal value.x = A*x + B*u; UU = [UU; u'];end

Compare Simulation Results

Plot the input and output signals from both the Simulink and command-line simulations along with the changing input and output bounds.

figure subplot(1,2,1) plot(0:Ts:Tsim,[UU uout.Data(:,1) uout.Data(:,2) uout.Data(:,3)]) grid title('Input') legend('Command-line input','Simulink input','Lower bound',...'Upper bound','Location','Southeast') subplot(1,2,2) plot(0:Ts:Tsim,[YY yout.Data(:,1) yout.Data(:,3)]) grid title('Output') legend('Command-line output','Simulink output','Upper bound',...'Location','Southeast')

The results of the command-line and Simulink simulations are the same. The MPC controller keeps the input and output signals within the specified bounds as the constraints change throughout the simulation.

bdclose(mdl)