RT-LAB vs. Model Predictive Control Toolbox

OPAL's RT-LAB is a real-time simulation platform that merges high performance with an improved user interface. This program is seamlessly integrated with MATLAB/Simulink®, enabling intricate model-based designs to effectively engage with real-world scenarios. It facilitates sophisticated real-time simulations across various fields, including aerospace, power electronics, and power systems. Nearly two decades ago, RT-LAB was initially utilized in the Canada Arm project for the Canadian Space Agency. Since then, it has transformed systems engineering across multiple domains, including space, terrestrial applications, and maritime environments. With RT-LAB, engineers and researchers can rapidly create innovative prototypes while conducting comprehensive testing essential for advancing new technologies. Furthermore, its versatility ensures that it remains a vital tool for tackling the challenges of modern engineering.

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.