MPCPy Description
MPCPy is a Python package which allows you to test and implement occupant-integrated models predictive control (MPC), for building systems. The package is focused on the use data-driven, simplified statistical or physical models to predict building performance and optimize control. Four modules provide object classes that allow you to import data, interact and validate models and control input. MPCPy is an integration platform. However, it relies upon third-party, free, open-source software packages to implement models, simulates, parameter estimation algorithms, optimization solvers, and other related tasks. This includes Python packages that can be used for data manipulation and scripting, as well as more advanced software packages for specific purposes. Modelica is the language specification that is used for optimization and modeling of physical systems.
MPCPy Alternatives
COLUMBO
Closed-loop universal multivariable optimizer for Model Predictive Control's (MPC), performance and quality improvements. You can use Excel files from Aspen Tech or Honeywell RMPCT (Robust Model Predictive Control Technology), or Predict Pro (Emerson) to create and improve the correct models for each MV-CV pair. This new optimization technology is not dependent on step tests, as Honeywell and Aspen tech require. It works in the time domain, is compact and practical, and is easy to use. Model Predictive Controls can have dozens or hundreds of dynamic models. One or more of these models could be wrong. Bad (wrong), Model Predictive Control dynamic models produce a bias between the predicted signal (model prediction error), and the measured signal from the sensor. COLUMBO can help you improve Model Predictive Control models (MPC) with either closed-loop or open-loop data.
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Pavilion8
Complex industrial processes make it difficult to be market-driven and profitable. Manufacturers need to adjust their production methods to offer a wider range of products with higher quality and shorter production runs. They must produce more, run more efficiently, and improve product quality within the limitations of their equipment. They must ensure maximum uptime, efficient transitions and less waste. Manufacturers are also being asked to reduce their environmental impact and comply with regulated emission limits. Rockwell Automation Pavilion8r Model Predictive Control technology (MPC) is an intelligence layer that sits on top of automation systems and continuously drives the plant to achieve multiple business goals, including cost reductions, decreased emissions, and production growth--all in real-time.
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Model Predictive Control Toolbox
Model Predictive control Toolbox™, which includes functions, an app, Simulink®, blocks, and references for the development of model predictive control (MPC), provides functions, an application, and Simulink®, blocks. The toolbox supports the creation of explicit, explicit, adaptive, gain-scheduled, and adaptive MPC for linear problems. Nonlinear problems can be solved by single- or multi-stage nonlinear MPC. The toolbox includes deployable optimization solvers, as well as the ability to create a custom solver. Closed-loop simulations can be used to evaluate controller performance in Simulink and MATLAB®. You can also use the MISRA C(r-)- and ISO 26262-compliant examples and blocks to automate driving. These blocks and examples are compatible with lane keep, path planning, following and adaptive cruise control applications. Design adaptive, gain-scheduled, or implicit MPC controllers that solve quadratic programming (QP). From an implicit design, generate an explicit MPC controller. For mixed-integer QP problems, use a discrete control set MPC.
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Cybernetica CENIT
Cybernetica offers Nonlinear Model Predictive Controller (NMPC) based upon mechanistic models. Cybernetica CENIT is a flexible software product that can address any industrial problem with optimal solutions. Multivariable optimal control, predictive control, intelligent feed forward, optimal constraint handling. Adaptive control via state and parameter estimation and feedback from indirect measurements through a process model. Nonlinear models can be used over larger operating ranges. Control of nonlinear processes can be improved. There is less need to perform step-response tests and there are better state and parameter estimates. Control of batch and semibatch processes, control over nonlinear processes that operate under varying conditions. Continuous processes require optimal grade transition. Safe control of exothermal process and control of unmeasured variables such as conversion rates, product quality, and other variables. Reduced energy consumption and carbon footprint
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Pricing
Pricing Starts At:
Free
Free Version:
Yes
Company Details
Company:
MPCPy
Headquarters:
United States
Website:
github.com/lbl-srg/MPCPy
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Product Details
Platforms
Linux
Type of Training
Documentation
Customer Support
Online
MPCPy Features and Options
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