Syndeia vs. Model Predictive Control Toolbox

A Digital Thread can be viewed as a network of nodes that symbolize different components located within enterprise repositories, systems, and tools for version control, with connections illustrating both internal relationships within each tool and external links that Syndeia enables between these elements. Furthermore, Syndeia provides model transformation features that are instrumental in building the digital thread graph, facilitating actions such as easily transferring requirements from Jama or DOORS-NG into SysML, creating Simulink models and PLM part structures based on SysML frameworks, linking behavioral components in SysML to relevant software code in GitHub, and even tracking the development status of a sub-system in JIRA directly from SysML. In addition, Syndeia enriches the digital thread by offering capabilities for searching, comparing, and bi-directionally synchronizing related models, which allows users to assess and align modifications in system architecture with the PLM part structure or to ensure that changes made in DOORS-NG requirements are reflected in SysML, showcasing the extensive functionalities involved in digital thread management. This interconnected framework not only enhances the efficiency of workflows but also guarantees that all project components are synchronized and current across various platforms, ultimately fostering better collaboration and project oversight. Thus, the comprehensive nature of the digital thread serves as a crucial element in modern engineering and project management strategies.

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.