MPCPy Description
MPCPy is a Python library designed to support the testing and execution of occupant-integrated model predictive control (MPC) within building systems. This tool emphasizes the application of data-driven, simplified physical or statistical models to forecast building performance and enhance control strategies. It comprises four primary modules that provide object classes for data importation, interaction with real or simulated systems, data-driven model estimation and validation, and optimization of control inputs. Although MPCPy serves as a platform for integration, it depends on various free, open-source third-party software for model execution, simulation, parameter estimation techniques, and optimization solvers. This encompasses Python libraries for scripting and data manipulation, along with more specialized software solutions tailored for distinct tasks. Notably, the modeling and optimization tasks related to physical systems are currently grounded in the specifications of the Modelica language, which enhances the flexibility and capability of the package. In essence, MPCPy enables users to leverage advanced modeling techniques through a versatile and collaborative environment.
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COLUMBO
A closed-loop universal multivariable optimizer is designed to enhance both the performance and quality of Model Predictive Control (MPC) systems. This optimizer utilizes data from Excel files sourced from Dynamic Matrix Control (DMC) by Aspen Tech, Robust Model Predictive Control Technology (RMPCT) from Honeywell, or Predict Pro from Emerson to develop and refine accurate models for various multivariable-controller variable (MV-CV) pairs. This innovative optimization technology eliminates the need for step tests typically required by Aspen Tech and Honeywell, operating entirely within the time domain while remaining user-friendly, compact, and efficient. Given that Model Predictive Controls (MPC) can encompass tens or even hundreds of dynamic models, the possibility of incorrect models is a significant concern. The presence of inaccurate dynamic models in MPCs leads to bias, which is identified as model prediction error, manifesting as discrepancies between predicted signals and actual measurements from sensors. COLUMBO serves as a powerful tool to enhance the accuracy of Model Predictive Control (MPC) models, effectively utilizing either open-loop or fully closed-loop data to ensure optimal performance. By addressing the potential for errors in dynamic models, COLUMBO aims to significantly improve overall control system effectiveness.
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Cybernetica CENIT
Cybernetica specializes in providing Nonlinear Model Predictive Control (NMPC) utilizing mechanistic models. Our innovative software solution, Cybernetica CENIT, features a versatile architecture capable of addressing diverse industrial challenges by delivering optimal strategies. This includes advanced multivariable optimal control, predictive control mechanisms, and intelligent feed-forward strategies, along with efficient handling of constraints. Furthermore, our adaptive control capabilities leverage state and parameter estimation, incorporating feedback from indirect measurements via the process model. The use of nonlinear models allows for effective operation across extensive ranges, enhancing the management of nonlinear processes. This leads to a diminished reliance on step-response experiments and bolstered accuracy in state and parameter estimations. Additionally, we offer control solutions for both batch and semi-batch operations, efficiently managing nonlinear processes that function under fluctuating conditions. Our technology also ensures optimal grade transitions in continuous operations, safe supervision of exothermic processes, and control of unmeasured variables, including conversion rates and product quality. As a result, we contribute to reduced energy consumption and a lower carbon footprint, while also enhancing overall process efficiency. In summary, Cybernetica is committed to advancing industrial control solutions that optimize performance and sustainability.
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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
Types of Training
Training Docs
Customer Support
Online Support
MPCPy Features and Options
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