Specify custom disturbance models, custom state estimator, terminal weights, and custom constraints
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.
Constraints and Weights
|Obtain mixed input/output constraints from model predictive controller|
|Set mixed input/output constraints for model predictive controller|
|Terminal weights and constraints|
|Obtain Kalman gains and model for estimator design|
|Modify a model predictive controller’s state estimator|
|Retrieve unmeasured input disturbance model|
|Retrieve unmeasured output disturbance model|
|Modify unmeasured input disturbance model|
|Modify unmeasured output disturbance model|
Custom QP Applications
|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|
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.
- Constraints on Linear Combinations of Inputs and Outputs
You can design and simulate a model predictive controller with mixed input/output constraints.
- 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.
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.
- Implement Custom State Estimator Equivalent to Built-In Kalman Filter
Design a state estimator equivalent to the linear Kalman filter of an MPC controller.
- 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.