Understanding Control Behavior by Examining Optimal Control Sequence
This example shows how to inspect the optimized sequence of manipulated variables computed by a model predictive controller at each sample time.
The plant is a double integrator subject to input saturation.
Design MPC Controller
The basic setup of the MPC controller includes:
A double integrator as the prediction model
A prediction horizon of
A control horizon of
-1 <= u(t) <= 1
Specify the MPC controller.
Ts = 0.1; p = 20; m = 10; mpcobj = mpc(tf(1,[1 0 0]),Ts,p,m); mpcobj.MV = struct('Min',-1,'Max',1); nu = 1;
-->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.
Simulate Model in Simulink
To run this example, Simulink® is required.
if ~mpcchecktoolboxinstalled('simulink') disp('Simulink is required to run this example.') return end
Open the Simulink model, and run the simulation.
mdl = 'mpc_sequence'; open_system(mdl) sim(mdl)
-->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.
The MPC Controller block has an
mv.seq output port, which is enabled by selecting the Optimal control sequence block parameter. This port outputs the optimal control sequence computed by the controller at each sample time. The output signal is an array with
p+1 rows and
Nmv columns, where
p is prediction horizon and
Nmv is the number of manipulated variables.
In a similar manner, the controller can output the optimal state sequence (
x.seq) and the optimal output sequence (
When the simulation stops, the
To Workspace block connected to the
mv.seq port exports this control sequence to the MATLAB® workspace, logging the data in the variable
Analyze Optimal Control Sequences
Plot the optimal control sequence at specific time instants.
times = [0 0.2 1 2 2.1 2.2 3 3.5 5]; figure('Name','Optimal sequence history') for t = 1:9 ct = times(t)*10+1; subplot(3,3,t) h = stairs(0:p,useq.signals.values(ct,:)); h.LineWidth = 1.5; hold on plot((0:p)+.5,useq.signals.values(ct,:),'*r') xlabel('prediction step') ylabel('u') title(sprintf('Sequence (t=%3.1f)',useq.time(ct))) grid axis([0 p -1.1 1.1]) hold off end
The MPC controller uses the first two seconds to bring the output very close to the set point. The controller output is at the upper limit (+1) for one second and switches to the lower limit (-1) for the next second, which is the best control strategy under the input constraints.