Weights and Constraints

Setting Targets for Manipulated Variables

If your plant has more manipulated variables than outputs, you can hold the excess manipulated variables at target values for economical or operational reasons.

Time-Varying Weights and Constraints

When designing an MPC controller, you can specify tuning weights and constraints that vary over the prediction horizon.

Constraints on Linear Combinations of Inputs and Outputs

You can design and simulate a model predictive controller with mixed input/output constraints.

• Use Custom Constraints in Blending Process

Terminal Weights and 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.

• Provide LQR Performance Using Terminal Penalty Weights

Disturbance Models and State Estimation

Adjust Disturbance and Noise Models

MPC controllers model unknown events using input and output disturbance models, and measurement noise models.

Custom State Estimation

You can override the default MPC controller state estimation method by changing the default Kalman gains or by supplying your own controller state estimates.

Optimization Settings

Manipulated Variable Blocking

You can improve the robustness of your controller and smooth manipulated variable adjustments by dividing the prediction horizon into a series of blocking intervals.

Specifying Alternative Cost Function with Off-Diagonal Weight Matrices

You can specify an alternative cost function for your model predictive controller to minimize during optimization.