Model Predictive Control Toolbox

Design and simulate model predictive controllers

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.

What Is Model Predictive Control Toolbox?

Chart showing linear MPC methods and when to use them.

Linear MPC Design

Design implicit, gain-scheduled, and adaptive MPC controllers that solve a quadratic programming (QP) problem. Generate an explicit MPC controller from an implicit design. Use discrete control set MPC for mixed-integer QP problems.

Understanding Model Predictive Control, Part 7: Adaptive MPC Design with Simulink and Model Predictive Control Toolbox (8:20)

Documentation | Examples

MPC Designer App

Use the MPC Designer app to interactively design implicit MPC controllers, linearize your Simulink model with Simulink Control Design™, validate controller performance using simulation scenarios, and compare responses for multiple designs.

Understanding Model Predictive Control, Part 6: How to Design an MPC Controller with Simulink and Model Predictive Control Toolbox (9:52)

Documentation | Examples

Simulink model with a Nonlinear MPC block.

Nonlinear MPC Design

Design nonlinear and economic MPC controllers that use Optimization Toolbox™ to solve a nonlinear programming (NLP) problem. Use single- or multi-stage formulation for optimal planning and feedback control.

Understanding Model Predictive Control, Part 8: Nonlinear MPC Design with Model Predictive Control Toolbox and FORCESPRO (18:09)

Documentation | Examples

Animated parking lot with a vehicle following a parking trajectory.

MPC Design for Automated Driving

Accelerate development of automated driving systems using prebuilt Simulink blocks that comply with ISO 26262 and MISRA C standards. Prebuilt blocks support path planning, path following, adaptive cruise control, and other applications.

Documentation | Examples

Chart of linear, nonlinear, and custom optimization solvers supported by Model Predictive Control Toolbox.

MPC Optimization Solvers

Select from built-in active-set, interior-point, and mixed-integer QP solvers, or use NLP solvers from Optimization Toolbox. Alternatively, use FORCESPRO solvers (by Embotech) or your own custom solver.

Understanding Model Predictive Control, Part 8: Nonlinear MPC Design with Model Predictive Control Toolbox and FORCESPRO (18:09)

Documentation

MPC controller illustration with a deep learning model used for prediction.

Prediction Model Specification

Specify prediction models analytically with Control System Toolbox™ or Symbolic Math Toolbox™, by linearizing a Simulink model with Simulink Control Design, or through measured data with System Identification Toolbox™ and Deep Learning Toolbox™.

Documentation

Report showing recommendations on MPC design parameters.

State Estimation and Design Review

Estimate controller states from measured outputs using the state estimator provided by the toolbox or a custom state estimator. Detect potential stability and robustness issues with your linear MPC design using the built-in diagnostic function.

Documentation | Examples

The Model Predictive Control Toolbox Library browser in Simulink.

Closed-Loop Simulation

Evaluate controller performance by running closed-loop simulations in Simulink using ISO 26262- and MISRA C-compliant Simulink blocks, as well as in MATLAB with command-line functions. Automate testing for multiple scenarios with Simulink Test™.

Documentation

Code Generation

Automatically generate production C/C++ and CUDA code, or IEC 61131-3 structured text, from MPC controllers designed in MATLAB and Simulink. Deploy the code to a variety of targets such as ECUs, GPUs, and PLCs.

Documentation | Examples

Product Resources:

Documentation Examples Videos Requirements Release notes Functions Blocks Technical articles

Sumitomo Heavy Industries Speeds Development of Embedded Model Predictive Control Software for Hydraulic Excavators

“With Model-Based Design, our hydraulics engineers can complete the controller design and implementation without involving an embedded engineer. That’s a big advantage, because it saves time and results in a higher-quality controller.”
Eisuke Matsuzaki, Sumitomo Heavy Industries

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