Simulink vs. Model Predictive Control Toolbox

Utilize Simulink to develop and test your system before transitioning to real hardware, giving you the chance to delve into and implement creative designs that might often be overlooked, all without needing to write C, C++, or HDL code. By creating models for both the system under evaluation and the physical plant, you can explore a wider range of design possibilities. Your entire team can take advantage of a cohesive multi-domain platform that simulates how all components of the system interact with one another. Additionally, you can compile and distribute your simulation outcomes with colleagues, suppliers, and clients, facilitating collaborative insights. This strategy significantly reduces the risk of expensive prototypes by enabling experimentation with scenarios that could be perceived as too uncertain or impractical. Incorporate hardware-in-the-loop testing and rapid prototyping to verify the success of your design. This methodology allows for traceability from the early stages of requirement gathering through to design and code generation. Instead of manually producing countless lines of code, you can automatically generate high-quality C and HDL code that reflects your original Simulink model. Ultimately, deploy this code onto your embedded processor or FPGA/ASIC for flawless integration and functionality. This thorough process not only simplifies development but also markedly improves efficiency across the entire project timeline, paving the way for enhanced innovation. Furthermore, it allows teams to quickly iterate on designs, leading to quicker time-to-market for new products.

The Model Predictive Control Toolbox™ provides an extensive array of functions, an easy-to-use app, Simulink® blocks, and useful reference examples to streamline the development of model predictive control (MPC) systems. It effectively addresses linear problems by allowing the development of implicit, explicit, adaptive, and gain-scheduled MPC approaches. For more intricate nonlinear situations, users can implement both single-stage and multi-stage nonlinear MPC. Moreover, this toolbox comes equipped with deployable optimization solvers and allows for the incorporation of custom solvers as needed. Users can evaluate the performance of their controllers through closed-loop simulations within MATLAB® and Simulink environments. In the context of automated driving, the toolbox offers blocks and examples that comply with MISRA C® and ISO 26262 standards, which facilitates the rapid start of projects related to lane keeping assistance, path planning, path following, and adaptive cruise control. It enables the design of implicit, gain-scheduled, and adaptive MPC controllers that can solve quadratic programming (QP) problems while also facilitating the generation of explicit MPC controllers based on implicit designs. Furthermore, the toolbox accommodates discrete control set MPC for addressing mixed-integer QP challenges, thus expanding its versatility for various control systems. With its rich set of features, the toolbox guarantees that both beginners and seasoned professionals can successfully apply advanced control strategies in their projects. This versatility ensures that users across multiple domains can find relevant applications for their specific needs.