Apromon

Model Predictive Control Toolbox™ provides functions, an app, Simulink® blocks, and reference examples for developing model predictive control (MPC). For linear problems, the toolbox supports the design of implicit, explicit, adaptive, and gain-scheduled MPC. For nonlinear problems, you can implement single- and multi-stage nonlinear MPC. The toolbox provides deployable optimization solvers and also enables you to use a custom solver. You can evaluate controller performance in MATLAB® and Simulink by running closed-loop simulations. For automated driving, you can also use the provided MISRA C®- and ISO 26262-compliant blocks and examples to quickly get started with lane keep assist, path planning, path following, and adaptive cruise control applications. Design implicit, gain-scheduled, and adaptive MPC controllers that solve a quadratic programming (QP) problem. Generate an explicit MPC controller from an implicit design. Use discrete control set MPC for mixed-integer QP problems.

Pitops is the only software product that performs truly closed-loop system identification with PID controllers in Auto mode or even of secondary PID controllers in a Cascade mode, without the need to break the cascade chain and to conduct additional time-consuming and intrusive plant step tests. No other competitor tool can do successful transfer function identification using data with PID controllers in Cascade mode (Pitops is the only one). Furthermore, Pitops performs transfer function identification entirely in the time domain whereas all other competitor tools use the more complicated Laplace (S) or Discrete (Z) domain. Pitops can even handle multiple inputs and identify multiple transfer functions simultaneously. Pitops performs multiple inputs closed-loop transfer function system identification in the time domain using a new proprietary breakthrough algorithm, far superior to the older methods like the ARX/ARMAX/Box and Jenkins methods that are used in competitor tools.