OnTrackPM vs. Model Predictive Control Toolbox

OnTrackPM distinguishes itself in the field of project management by adeptly managing tasks that range from initial research phases to extensive projects. Integrating OnTrackPM into your toolkit will not only boost your team's efficiency but also greatly reduce the chances of data errors. With a wealth of international experience, we confidently address your specific challenges through our tailored, tested framework designed for your unique project requirements. Our approach emphasizes practical methodologies that align closely with your business goals, consistently exceeding expectations and increasing the value we deliver in every endeavor. Managing collaboration among diverse stakeholders and systems can often lead to mistakes and setbacks. To mitigate these risks, OnTrackPM offers in-depth insights into the information flow, focusing on critical areas that are vital for the success of any project. Our adaptable strategy, backed by years of industry knowledge, promotes collaboration and accountability within your diverse team, enabling efficient oversight of your projects and operations. In addition, OnTrackPM prioritizes clear and effective communication with both the project team and external vendors, diligently monitoring key metrics and essential components to sustain project momentum. Ultimately, our dedication to transparency and accuracy not only fosters successful project outcomes but also builds a foundation for future collaborations. With OnTrackPM, you're not just managing projects; you're paving the way for excellence.

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.