Apromon
Apromon is an online software product for monitoring the PID loop control performance of primary and Advanced Process Control (APC) loops. Apromon evaluates single loops, cascade loops, any Advanced Process Control (APC) loops and even signals that have PV only but no controller associated with them. Apromon has the unique power to automatically convert flow controllers, pressure controllers, temperature controllers, level controllers, online analysis controllers, and any Advanced Process Control (APC) controller into a single “grade” factor, just like the grade given by a professor to a student on a test or an examination. 100 indicates the best performance and 0 indicates the worst. Runs automatically every set period so that performance is always being calculated and archived. Runs all the time, and does not skip any period for any tag like some competitor products.
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Model Predictive Control Toolbox
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.
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MPCPy
MPCPy is a Python package that facilitates the testing and implementation of occupant-integrated model predictive control (MPC) for building systems. The package focuses on the use of data-driven, simplified physical or statistical models to predict building performance and optimize control. Four main modules contain object classes to import data, interact with real or emulated systems, estimate and validate data-driven models, and optimize control input. While MPCPy provides an integration platform, it relies on free, open-source, third-party software packages for model implementation, simulators, parameter estimation algorithms, and optimization solvers. This includes Python packages for scripting and data manipulation as well as other more comprehensive software packages for specific purposes. In particular, modeling and optimization for physical systems currently rely on the Modelica language specification.
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INCA MPC
Advanced Process Control (APC) is a very cost-effective way to optimize your plant performance without changing the hardware. An APC application stabilizes the operation and optimizes production and/or energy consumption. A very valuable side effect also results in a better understanding of your production process. Advanced process control (APC) refers to a broad range of techniques and technologies that interact with the base layer process control systems (built up with PID controls). Some APC technologies are e.g. LQR, LQC, H_infinity, Neural, fuzzy, and MPC (Model-Based Predictive Control). An APC application optimizes your plant every minute, over and over again, 24 hours per day, 7 days per week. MPC is the most popular APC technology used in the industry. The Model Predictive Control software uses a model of the process to predict the behavior of the plant in the foreseeable future. Typically a couple of minutes to even several hours ahead.
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