RFPro Circuit vs. Model Predictive Control Toolbox

Elevate your capabilities in RF simulation to proficiently design, assess, and validate radio frequency integrated circuits (RFICs) beyond conventional techniques. Achieve reliable results by utilizing steady-state and nonlinear solvers specifically designed for both the design and verification phases. Speed up the validation process of complex RFICs with wireless standard libraries that prioritize efficiency. Ensure accurate modeling of silicon chip components to attain the highest precision possible. Improve your designs with load-pull analysis and parameter sweeps to achieve superior performance outcomes. Execute RF simulations within the Cadence Virtuoso and Synopsys Custom Compiler environments to optimize your workflow. Incorporate Monte Carlo simulations and yield analysis into your strategy to enhance performance metrics further. At the outset of the design process, assess error vector magnitude (EVM) to align with current communication standards, ensuring your designs remain compliant. Capitalize on innovative foundry technology right from the beginning of your project. It becomes imperative to monitor critical specifications like EVM through RF simulation during the initial stages of RFIC design. These simulations factor in the implications of layout parasitics, complex modulated signals, and digital control circuitry. By utilizing Keysight RFPro Circuit, you gain the capability for thorough simulation across both frequency and time domains, significantly improving the overall design process and accuracy. This comprehensive strategy guarantees that your RFICs not only meet but surpass industry benchmarks, paving the way for future advancements in technology. Ultimately, embracing such an approach will position your designs at the forefront of innovation in the RFIC sector.

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.