Model Predictive Control Toolbox

Using Custom Input and Output Constraints

This example shows how to design model predictive controller with mixed input/output constraints.

Setup MPC Controller

The basic setup of the MPC controller includes:

  • A double integrator as the prediction model

  • Prediction horizon of 20

  • Control horizon of 20

  • Input constraints -1 <= u(t) <= 1

Define the MPC Controller setup:

model = tf(1,[1 0 0]);                          % Prediction model
Ts = .1;                                        % Sampling time
p = 20;                                         % Prediction horizon
m = 20;                                         % Control horizon
mpc_controller = mpc(model,Ts,p,m);             % MPC object
mpc_controller.MV = struct('Min',-1,'Max',1);   % Input saturation constraints
-->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.

Define Mixed Input/Output (I/O) Constraint

The sum of the input u(t) and output y(t) must be nonnegative and smaller than 1.2:

                0 <= u(t) + y(t) <= 1.2

To impose this combined (mixed) I/O constraint, formulate it as a set of inequality constraints involving u(t) and y(t):

                u(t) +  y(t) <= 1.2
               -u(t) + -y(t) <= 0

Simulate Model in Simulink®

Define initial conditions:

x01 = 0;                                        % Initial state: First integrator
x02 = 0;                                        % Initial state: Second integrator

Open and simulate the Simulink® model:

if ~mpcchecktoolboxinstalled('simulink')
    disp('Simulink(R) is required to run this example.')
open_system('mpc_mixedconstraints');            % Open Simulink(R) Model
sim('mpc_mixedconstraints');                    % Start Simulation
-->Converting the "Model.Plant" property of "mpc" object to state-space.
-->Converting model to discrete time.
-->The "Model.Noise" property of the "mpc" object is empty. Assuming white noise on each measured output channel.

As you can see, the MPC Controller always keeps the sum u+y between 0 and 1.2 while tracking the reference signal r=1.