Model Predictive Control Toolbox
Model Predictive Control Toolbox™ provides functions, an app, Simulink® blocks, and reference examples for developing model predictive control (MPC). For linear problems, the toolbox supports the design of implicit, explicit, adaptive, and gain-scheduled MPC. For nonlinear problems, you can implement single- and multi-stage nonlinear MPC. The toolbox provides deployable optimization solvers and also enables you to use a custom solver.
You can evaluate controller performance in MATLAB® and Simulink by running closed-loop simulations. For automated driving, you can also use the provided MISRA C™- and ISO 26262-compliant blocks and examples to quickly get started with lane keep assist, path planning, path following, and adaptive cruise control applications.
The toolbox supports C and CUDA® code and IEC 61131-3 Structured Text generation.
Learn the basics of Model Predictive Control Toolbox
Linear Plant Specification
Specify linear plant model, input and output signal types, scale factors
Basic workflow for designing traditional (implicit) model predictive controllers
Explicit MPC Design
Fast model predictive control using precomputed solutions instead of run-time optimization
Adaptive MPC Design
Adaptive control of nonlinear plant by updating internal plant model at run time
Gain-Scheduled MPC Design
Gain-scheduled control of nonlinear plants by switching controllers at run time
Nonlinear MPC Design
Design model predictive controllers with nonlinear prediction models, costs, and constraints
Generate code and deploy controllers on real-time targets
Automated Driving Applications
Design and simulate model predictive controllers for automated driving