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Model Predictive Control Toolbox

Adjusting Run-Time Controller Performance

Model Predictive Control Toolbox supports monitoring run-time controller performance and adjusting run-time tuning parameters.

Monitoring Run-Time Controller Performance

Model predictive controllers formulate and solve a QP optimization problem at each computation step. The QP solver supplied with the toolbox is optimized for performance and robustness. It achieves convergence even when the optimization problem is ill-conditioned.

For rare occasions when the optimization may fail to converge due to process abnormalities, the MPC Controller block lets you monitor optimization status at run time. You can access the optimization status signal to detect when an optimization fails to converge, and decide if a backup control strategy should be used.

The MPC Controller block also lets you access the optimal cost and optimal control sequence at each computation step. You can use these signals to analyze controller performance and to develop custom control strategies. For example, you may use optimal cost information for switching between two model predictive controllers whose outputs are restricted to discrete values.

Simulink model that uses the optimal cost signal to switch between two model predictive controllers whose outputs are restricted to discrete values.
Simulink model that uses the optimal cost signal to switch between two model predictive controllers whose outputs are restricted to discrete values. You can compare the reference signal (top right, red) and plant output (top right, blue) to evaluate controller performance, and you can plot the manipulated variable (controller output) to see when the control strategy switches between controllers.

Adjusting Run-Time Tuning Parameters

The toolbox lets you adjust the run-time tuning parameters of your model predictive controller to optimize its performance at run time without redesigning or reimplementing it. To perform run-time controller tuning in Simulink, you configure the MPC Controller block to accept the appropriate run-time tuning parameters. You can also perform run-time controller tuning in MATLAB.

Model Predictive Control Toolbox provides access to the following run-time tuning parameters:

  • Weights on plant outputs
  • Weights on manipulated variables
  • Weight on overall constraint softening
Simulink model for run-time tuning of model predictive controller parameters.
Simulink model for run-time tuning of model predictive controller parameters. Model Predictive Control Toolbox enables run-time tuning by changing weights on plant outputs, weights on manipulated variables, and the weight on overall constraint softening.
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