Best Model Predictive Control (MPC) Software with a Free Trial

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.

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.

Closed-loop universal multivariable optimizer for Model Predictive Control (MPC) performance and Model Predictive Control (MPC) quality improvements. Use data in Excel files from DMC (Dynamic Matrix Control) from Aspen Tech, or from RMPCT (Robust Model Predictive Control Technology) from Honeywell, or Predict Pro from Emerson and use it to generate and improve correct models for the various MV-CV pairs. Amazing new optimization technology does not need step tests as required by Aspen tech, Honeywell, and others. It Works entirely in the time domain, is easy to use, compact, and practical. Model Predictive Controls (MPC) can have 10s or 100s of dynamic models. One or more could be wrong. Bad (wrong) Model Predictive Control (MPC) dynamic models produce a bias (model prediction error) between the predicted signal and the measured signal coming from the sensor. COLUMBO will help you to improve Model Predictive Control (MPC) models with either open-loop or completely closed-loop data.