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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AVEVA APC
AVEVA APC is a model predictive advanced control system that improves process economics. Manufacturers are facing increasing overhead costs and reduced capital budgets in today's economic environment. They also face rising manufacturing and energy prices and fierce global competition. Comprehensive Advanced Process Control by AVEVA helps you solve complex manufacturing problems with state-of the-art automated control solutions that can extract maximum value out of your processes. It can increase production yield, quality, and reduce energy consumption. It can optimize manufacturing operations and provide the performance improvements that you need to improve your bottom-line. AVEVA APC is a comprehensive, predictive, advanced process control software that increases process profitability by improving quality, increasing throughput and reducing energy consumption. It utilizes state-of the-art technology to create automatic control systems capable of unlocking process potential.
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INCA MPC
Advanced Process Control (APC), a cost-effective method to optimize your plant's performance without having to change the hardware, is very cost-effective. APC applications stabilize the operation and optimize production and/or energy use. An important side effect is a better understanding of your production process. Advanced process control (APC), refers to a wide range of technologies and techniques that interact with the base process control systems (built with PID controls). APC technologies include e.g. LQR and LQC, H_infinity, neural, fuzzy, and Model-Based Predictive Controller (MPC) are some examples of APC technologies. An APC application optimizes every minute of your plant, 24 hours a day, 7 days a week. MPC is the most widely used APC technology in the industry. Model Predictive Control software uses a model to predict the plant's behavior in the future. It can usually be done in a matter of minutes or even hours.
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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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