Once you have created a model predictive controller for your plant, you can tune the system closed-loop response using the MPC Designer app or at the command line.
|MPC Designer||Design and simulate model predictive controllers|
If your plant has more manipulated variables than outputs, you can hold the excess manipulated variables at target values for economical or operational reasons.
When designing an MPC controller, you can specify tuning weights and constraints that vary over the prediction horizon.
You can constrain linear combinations of plant input and output variables. For example, you can constrain a particular manipulated variable to be greater than a linear combination of two other manipulated variables.
Design a model predictive controller using custom input and output constraints.
To achieve infinite horizon control, you can use terminal weights at the final prediction horizon step. To ensure stability for constrained systems, you may have to also define terminal constraints at the end of the prediction horizon.
It is possible to make a finite-horizon model predictive controller equivalent to an infinite-horizon linear quadratic regulator by using terminal penalty weights.
MPC controllers model unknown events using input and output disturbance models, and measurement noise models.
You can override the default MPC controller state estimation method by changing the default Kalman gains or by supplying your own controller state estimates.
To use manipulated variable blocking, divide the prediction horizon into a series of blocks. The controller then holds the manipulated variable constant within each block.
You can specify an alternative cost function for your model predictive controller to minimize during optimization.