SmartDX vs. Model Predictive Control Toolbox

SmartDX revolutionizes the way organizations track and identify individual audience members from their very first interaction, allowing for the detailed mapping of their experiences across multiple devices and fostering contextual omnichannel engagements while attributing conversions at an exceptionally personalized segment-of-one level. This groundbreaking solution serves as a powerful substitute for conventional cookies, effectively minimizing delays in contextual responses and removing obstacles within customer journeys. It captures distinctive insights tied to individual experiences and various audience characteristics, such as device type, channel engagement, conversion metrics, promotional offers, and both implicit and explicit behavioral insights right at the moment of interaction and across different touchpoints. Additionally, SmartDX effectively differentiates between known and unknown users, addressing the challenge of multiple digital identities for each audience member across various channels. Through the use of progressive profiling during interactions, it consistently enhances existing audience profiles, laying the groundwork for personalized communications and bespoke audience experiences. Furthermore, it guarantees fluid transitions throughout omnichannel interactions, resulting in seamless journeys that are informed by previous engagements and individual preferences. Ultimately, this comprehensive approach leads to a marketing strategy that is not only more personalized but also significantly more effective, ensuring that it resonates deeply with each audience member on a personal level. By focusing on creating a tailored experience, businesses can build stronger relationships with their audiences.

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.