Enhance Control System Design with the Model Predictive Control Toolbox from MathWorks

About Model Predictive Control Toolbox

The Model Predictive Control Toolbox from MathWorks is an exceptional resource for engineers and scientists looking to enhance their control system design capabilities. This toolbox stands out for its comprehensive suite of functions, apps, and Simulink blocks that facilitate the development of both linear and nonlinear model predictive controllers (MPC).

One of the most impressive features is the ability to design various types of MPC, including implicit, explicit, adaptive, and gain-scheduled controllers. The toolbox also supports advanced applications in automated driving, providing prebuilt blocks that comply with industry standards such as ISO 26262 and MISRA C. This makes it an invaluable tool for professionals working in cutting-edge fields like autonomous vehicles.

The user-friendly MPC Designer app allows for interactive design and validation of controllers, making it accessible even for those who may not be experts in control theory. The closed-loop simulation capabilities in MATLAB and Simulink enable users to evaluate controller performance effectively, ensuring that designs meet the necessary performance criteria before deployment.

Moreover, the toolbox's support for code generation means that users can seamlessly transition from design to implementation, generating production-ready C/C++ and CUDA code. This feature is particularly beneficial for those looking to deploy their solutions on various hardware platforms, including ECUs and GPUs.

Customer testimonials, such as the one from Sumitomo Heavy Industries, highlight the tangible benefits of using the Model Predictive Control Toolbox, including significant reductions in fuel consumption and improved dynamic performance.

the Model Predictive Control Toolbox is a powerful and versatile tool that empowers engineers to design sophisticated control systems with ease. Its robust features, combined with excellent support and resources, make it a top choice for anyone serious about advancing their control system design capabilities.