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.
|Solve quadratic programming problem using active-set algorithm|
|Create default option set for |
|Solve a quadratic programming problem using an interior-point algorithm|
|Create default option set for
|Configures an MPC object to use the QP solver from Optimization Toolbox as a custom solver|
|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 design and simulate a model predictive controller with mixed input/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.
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.
Design a state estimator equivalent to the linear Kalman filter of an MPC controller.
You can improve the robustness of your controller and smooth manipulated variable adjustments by dividing the prediction horizon into a series of blocking intervals.
You can specify an alternative cost function for your model predictive controller to minimize during optimization.