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What is MPCPy?
MPCPy is a Python-based library specifically created to facilitate the testing and implementation of occupant-integrated model predictive control (MPC) in building systems. This innovative tool focuses on utilizing data-driven, simplified physical or statistical models to predict the performance of buildings and improve control methodologies. It consists of four key modules that offer object classes for tasks such as data importation, engagement with either real or simulated systems, estimation and validation of data-driven models, and optimization of control inputs. While MPCPy acts as a comprehensive integration platform, it relies on a variety of free, open-source third-party software for executing models, conducting simulations, implementing parameter estimation techniques, and optimizing solvers. This includes Python libraries for scripting and data manipulation, as well as specialized software solutions designed for specific functions. Importantly, the tasks involving modeling and optimization of physical systems are currently based on the requirements of the Modelica language, which significantly enhances the package's flexibility and capabilities. Overall, MPCPy empowers users to harness sophisticated modeling methods within a dynamic and cooperative environment, ultimately fostering improved building system performance. Furthermore, it opens up opportunities for researchers and practitioners alike to experiment with cutting-edge control strategies tailored to real-world scenarios.
What is Cybernetica CENIT?
Cybernetica is dedicated to delivering Nonlinear Model Predictive Control (NMPC) by leveraging mechanistic models. Our cutting-edge software, Cybernetica CENIT, boasts a flexible architecture designed to tackle a wide array of industrial challenges while producing optimal control strategies. This encompasses sophisticated multivariable optimal control, predictive control techniques, and smart feed-forward methods, all while adeptly managing various constraints. Additionally, our adaptive control features utilize state and parameter estimation, allowing for the integration of feedback derived from indirect measurements through the process model. Employing nonlinear models facilitates effective performance across broad operational ranges, significantly improving the management of complex nonlinear processes. Consequently, this approach reduces the dependence on step-response experiments and enhances the precision of state and parameter estimations. Moreover, we provide tailored control solutions for both batch and semi-batch operations, efficiently overseeing nonlinear processes that endure varying conditions. Our technology also guarantees optimal transitions in product grades during continuous operations, ensures the safe management of exothermic reactions, and controls unmeasured variables such as conversion rates and product quality. Ultimately, these advancements lead to decreased energy consumption and a minimized carbon footprint, while simultaneously boosting overall process efficiency. In conclusion, Cybernetica is fully committed to pioneering industrial control solutions that not only enhance performance but also promote sustainability in various sectors. Our relentless pursuit of innovation positions us as leaders in the field, enabling us to adapt to the evolving needs of our clients.
Integrations Supported
Python
Ubuntu
API Availability
Has API
API Availability
Has API
Pricing Information
Free
Free Trial Offered?
Free Version
Pricing Information
Pricing not provided.
Free Trial Offered?
Free Version
Supported Platforms
SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux
Supported Platforms
SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux
Customer Service / Support
Standard Support
24 Hour Support
Web-Based Support
Customer Service / Support
Standard Support
24 Hour Support
Web-Based Support
Training Options
Documentation Hub
Webinars
Online Training
On-Site Training
Training Options
Documentation Hub
Webinars
Online Training
On-Site Training
Company Facts
Organization Name
MPCPy
Company Location
United States
Company Website
github.com/lbl-srg/MPCPy
Company Facts
Organization Name
Cybernetica
Company Location
Norway
Company Website
cybernetica.no/technology/model-predictive-control/