PathWave RFIC Design vs. Model Predictive Control Toolbox

Enhance your methodology for RF simulation by emphasizing the thorough design, scrutiny, and validation of radio frequency integrated circuits (RFICs). Ensure confidence in your projects by leveraging steady-state and nonlinear solvers during both the design and verification stages. Utilizing wireless standard libraries significantly accelerates the process of validating complex RFICs. It is vital to verify IC specifications through RF simulation before finalizing an RFIC, as these simulations account for various elements, including layout parasitics, complex modulated signals, and digital control circuitry. With PathWave RFIC Design, you can conduct simulations across both frequency and time domains, allowing for effortless transitions between your designs and Cadence Virtuoso. Achieve precise modeling of components on silicon chips, and refine your designs by employing optimization techniques such as sweeps and load-pull analysis. The integration of RF designs into the Cadence Virtuoso ecosystem is made more efficient, while the application of Monte Carlo and yield analysis can significantly enhance overall performance. Furthermore, debugging is simplified through safe operating area alerts, enabling the quick adoption of state-of-the-art foundry technologies to maintain a competitive edge in innovation. This comprehensive strategy for RFIC design not only boosts efficiency but also significantly enhances the overall quality and dependability of the resultant products, making it a crucial element in modern electronic design. By adopting this approach, engineers can achieve greater precision and reliability in their RFIC projects, ultimately leading to more successful outcomes in various applications.

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.