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