Documentation

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

Apps

MPC Designer Design and simulate model predictive controllers
Was this topic helpful?