Main Content

Analysis

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.

Functions

expand all

reviewExamine MPC controller for design errors and stability problems at run time
compareCompare two MPC objects
cloffsetCompute MPC closed-loop DC gain from output disturbances to measured outputs assuming constraints are inactive at steady state
sensitivityCompute effect of controller tuning weights on performance
sizeSize and order of MPC Controller
trimCompute steady-state value of MPC controller state for given inputs and outputs
d2dChange MPC controller sample
ssConvert unconstrained MPC controller to state-space linear system
tfConvert unconstrained MPC controller to linear transfer function
zpkConvert unconstrained MPC controller to zero/pole/gain form

Topics

Design Review

Review Model Predictive Controller for Stability and Robustness Issues

You can detect potential issues with your MPC controller design at the command line and using MPC Designer.

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, 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.

Adjust Input and Output Weights Based on Sensitivity Analysis

Compute numerical derivatives of a closed-loop cumulated performance index with respect to weights and use them to improve model predictive controller performance.

Understanding Control Behavior by Examining Optimal Control Sequence

You can analyze the optimal control sequence computed by a model predictive controller at each sample time.