Best Advanced Process Control (APC) Systems for Linux

Epicor Connected Process Control (CPC), formerly eFlex Systems, provides manufacturers a flexible, no-code/low-code MES solution. No programming or special skills required. Digital work instructions, with multi-media capabilities, along with the ability to integrate virtually any device with communication capabilities, provides 100% historical record of the product and the process. Providing data insight, from production reports, to part history, quality summary and more — address issues quickly, minimize waste and disruptions. Whether you start small in subassembly areas, an entire line, or apply enterprise wide - we work with manufacturers of all sizes and needs. Hosted on prem or in the cloud, you decide what's best for your operations.

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.

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.