Model Predictive Control Toolbox

Overview

Model Predictive Control Toolbox™ provides tools for systematically analyzing, designing, and tuning model predictive controllers. You can design and simulate model predictive controllers using functions in MATLAB^® or blocks in Simulink^®. You can set and modify the predictive model, control and prediction horizons, input and output constraints, and weights. The toolbox enables you to diagnose issues that could lead to run-time failures and provides advice on changing weights and constraints to improve performance and robustness. By running different scenarios in linear and nonlinear simulations, you can evaluate controller performance. You can adjust controller performance as it runs by tuning weights and varying constraints. For rapid prototyping and embedded system design, the toolbox supports C-code generation.

Key Features

• Design and simulation of model predictive controllers in MATLAB and Simulink
• Customization of constraints and weights with advisory tools for improved performance and robustness
• Control of plants over a range of operating conditions using multiple model predictive controllers with bumpless control transfer
• Run-time adjustment of controller performance through constraint and weight changes
• Specialized model predictive control quadratic programming (QP) solver optimized for speed, efficiency, and robustness
• Support for C-code generation with Simulink Coder™

MPC Controller block (red) for designing and simulating model predictive controllers directly in Simulink.