Model Predictive Control Toolbox™ provides functions, an app, and Simulink® blocks for designing and simulating model predictive controllers (MPCs). The toolbox lets you specify plant and disturbance models, horizons, constraints, and weights. By running closed-loop simulations, you can evaluate controller performance.
You can adjust the behavior of the controller by varying its weights and constraints at run time. To control a nonlinear plant, you can implement adaptive and gain-scheduled MPCs. For applications with fast sample rates, you can generate an explicit model predictive controller from a regular controller or implement an approximate solution.
For rapid prototyping and embedded system implementation, the toolbox supports C-code and IEC 61131-3 Structured Text generation.
Optimize closed-loop system performance of MIMO plants subject to input and output constraints.Learn more
Optimize controller performance by adjusting constraints and weights, and by customizing QP solver and state estimation.Learn more
Design adaptive and implement gain-scheduled MPC controllers.Learn more
Discover more about Model Predictive Control Toolbox by exploring these resources.
Explore documentation for Model Predictive Control Toolbox functions and features, including release notes and examples.
Browse the list of available Model Predictive Control Toolbox functions.
View a Simulink library of blocks that Model Predictive Control Toolbox supports.
View system requirements for the latest release of Model Predictive Control Toolbox.
View articles that demonstrate technical advantages of using Model Predictive Control Toolbox.
Read how Model Predictive Control Toolbox is accelerating research and development in your industry.
Find answers to questions and explore troubleshooting resources.
Model Predictive Control Toolbox apps enable you to quickly access common tasks through an interactive interface.
Use Model Predictive Control Toolbox to solve scientific and engineering challenges: