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 automatic C-code and IEC 61131-3 Structured Text generation.
App for interactive design of MPC controllers
Runtime adjustment of weights and constraints
Adaptive and gain-scheduled MPCs for controlling systems with nonlinear dynamics
Explicit MPC and approximate solution for guaranteed worst-case execution time
Economic MPC with arbitrary nonlinear cost function and constraints
Computationally efficient quadratic programming (QP) solver, and support for third-party solvers
Support for C-code generation (with Simulink Coder™) and IEC 61131-3 Structured Text generation (with Simulink PLC Coder™)