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Review run-time design errors and stability issues, analyze effect of weights on performance, convert unconstrained controller for linear analysis

Once you have created and designed your model predictive controller, you can review it for potential design issues. For more information, see Review Model Predictive Controller for Stability and Robustness Issues.


review Examine MPC controller for design errors and stability problems at run time
compare Compare two MPC objects
cloffset Compute MPC closed-loop DC gain from output disturbances to measured outputs assuming constraints are inactive at steady state
sensitivity Compute effect of controller tuning weights on performance
size Size and order of MPC Controller
trim Compute steady-state value of MPC controller state for given inputs and outputs
d2d Change MPC controller sample
ss Convert unconstrained MPC controller to state-space linear system
tf Convert unconstrained MPC controller to linear transfer function
zpk Convert unconstrained MPC controller to zero/pole/gain form


Design Review

Review Model Predictive Controller for Stability and Robustness Issues

Detect potential issues with your MPC controller design at the command line.

Test Controller Robustness

It is good practice to test the robustness of your model predictive controller to prediction errors.

Additional Validation

Compute Steady-State Gain

Compute the closed-loop, steady-state gain for each output when a sustained, 1-unit disturbance is added to each output.

Extract Controller

Obtain a linear state-space model of an unconstrained MPC controller. You can use this model to analyze the frequency response and performance of the controller.

Compare Multiple Controller Responses Using MPC Designer

You can compare the time-domain and frequency-domain responses of multiple MPC controller designs.

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