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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.


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getGet property values from MPC object
getnameRetrieve I/O signal names from MPC plant model
setSet or modify MPC object properties
setnameSet I/O signal names in MPC plant model
getconstraintObtain mixed input/output constraints from model predictive controller
setconstraintSet mixed input/output constraints for model predictive controller
setterminalTerminal weights and constraints
getEstimatorObtain Kalman gains and model for estimator design
setEstimatorModify a model predictive controller’s state estimator
getindistRetrieve unmeasured input disturbance model
getoutdistRetrieve unmeasured output disturbance model
setindistModify unmeasured input disturbance model
setoutdistModify unmeasured output disturbance model
mpcActiveSetSolverSolve quadratic programming problem using active-set algorithm (Since R2020a)
mpcActiveSetOptionsCreate default option set for mpcActiveSetSolver (Since R2020a)
mpcInteriorPointSolverSolve a quadratic programming problem using an interior-point algorithm (Since R2020a)
mpcInteriorPointOptionsCreate default option set for mpcInteriorPointSolver (Since R2020a)
setCustomSolverConfigures an MPC object to use the QP solver from Optimization Toolbox as a custom solver (Since R2021b)


MPC DesignerDesign and simulate model predictive controllers


Weights and Constraints

Disturbance Models and State Estimation

Optimization Settings