Documentation

This is machine translation

Translated by Microsoft
Mouse over text to see original. Click the button below to return to the English verison of the page.

Refinement

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.

Functions

get MPC property values
getname Retrieve I/O signal names in MPC prediction model
set Set or modify MPC object properties
setname Set I/O signal names in MPC prediction model
getconstraint Set custom constraints on linear combinations of plant inputs and outputs
setconstraint Set custom constraints on linear combinations of plant inputs and outputs
setterminal Terminal weights and constraints
getEstimator Obtain Kalman gains and model for estimator design
setEstimator Modify a model predictive controller's state estimator
getindist Retrieve unmeasured input disturbance model
getoutdist Retrieve unmeasured output disturbance model
setindist Modify unmeasured input disturbance model
setoutdist Modify unmeasured output disturbance model

Apps

MPC Designer Design and simulate model predictive controllers

Topics

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 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.

Use Custom Constraints in Blending Process

Design a model predictive controller using custom input and 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.

Provide LQR Performance Using Terminal Penalty Weights

It is possible to make a finite-horizon model predictive controller equivalent to an infinite-horizon linear quadratic regulator by using terminal penalty weights.

Disturbance Models and State Estimation

Adjusting 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

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.

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.

Was this topic helpful?