About
MPCPy is a Python package that facilitates the testing and implementation of occupant-integrated model predictive control (MPC) for building systems. The package focuses on the use of data-driven, simplified physical or statistical models to predict building performance and optimize control. Four main modules contain object classes to import data, interact with real or emulated systems, estimate and validate data-driven models, and optimize control input. While MPCPy provides an integration platform, it relies on free, open-source, third-party software packages for model implementation, simulators, parameter estimation algorithms, and optimization solvers. This includes Python packages for scripting and data manipulation as well as other more comprehensive software packages for specific purposes. In particular, modeling and optimization for physical systems currently rely on the Modelica language specification.
|
About
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.
|
About
Pitops is the only software product that performs truly closed-loop system identification with PID controllers in Auto mode or even of secondary PID controllers in a Cascade mode, without the need to break the cascade chain and to conduct additional time-consuming and intrusive plant step tests. No other competitor tool can do successful transfer function identification using data with PID controllers in Cascade mode (Pitops is the only one). Furthermore, Pitops performs transfer function identification entirely in the time domain whereas all other competitor tools use the more complicated Laplace (S) or Discrete (Z) domain. Pitops can even handle multiple inputs and identify multiple transfer functions simultaneously. Pitops performs multiple inputs closed-loop transfer function system identification in the time domain using a new proprietary breakthrough algorithm, far superior to the older methods like the ARX/ARMAX/Box and Jenkins methods that are used in competitor tools.
|
About
Enjoy the highest performance and unlimited possibilities when working with SQL Server. SQL Server Data Access Components (SDAC) is a library of components that provides native connectivity to SQL Server from Delphi and C++Builder including Community Edition, as well as Lazarus (and Free Pascal) for Windows, Linux, macOS, iOS, and Android for both 32-bit and 64-bit platforms. SDAC-based applications connect to SQL Server directly through OLE DB, which is a native SQL Server interface. SDAC is designed to help programmers develop faster and cleaner SQL Server database applications. SDAC, a high-performance, and feature-rich SQL Server connectivity solution is a complete replacement for standard SQL Server connectivity solutions and presents an efficient native alternative to the Borland Database Engine (BDE) and standard dbExpress driver for access to SQL Server. SDAC-based DB applications are easy to deploy, and do not require the installation of other data provider layers.
|
|||
Platforms Supported
Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook
|
Platforms Supported
Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook
|
Platforms Supported
Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook
|
Platforms Supported
Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook
|
|||
Audience
Plants and companies requiring an open-source platform to improve their Model Predictive Control (MPC) in their buildings
|
Audience
Companies looking for a solution to design and simulate model predictive controllers
|
Audience
Organizations looking for an advanced process control system that helps identify process dynamics using plant data
|
Audience
Programmers in need of a tool to develop faster and cleaner SQL Server database applications
|
|||
Support
Phone Support
24/7 Live Support
Online
|
Support
Phone Support
24/7 Live Support
Online
|
Support
Phone Support
24/7 Live Support
Online
|
Support
Phone Support
24/7 Live Support
Online
|
|||
API
Offers API
|
API
Offers API
|
API
Offers API
|
API
Offers API
|
|||
Screenshots and Videos |
Screenshots and Videos |
Screenshots and Videos |
Screenshots and Videos |
|||
Pricing
Free
Free Version
Free Trial
|
Pricing
$1,180 per year
Free Version
Free Trial
|
Pricing
No information available.
Free Version
Free Trial
|
Pricing
$199.95 per year
Free Version
Free Trial
|
|||
Reviews/
|
Reviews/
|
Reviews/
|
Reviews/
|
|||
Training
Documentation
Webinars
Live Online
In Person
|
Training
Documentation
Webinars
Live Online
In Person
|
Training
Documentation
Webinars
Live Online
In Person
|
Training
Documentation
Webinars
Live Online
In Person
|
|||
Company InformationMPCPy
United States
github.com/lbl-srg/MPCPy
|
Company InformationMathWorks
United States
www.mathworks.com/products/model-predictive-control.html
|
Company InformationPiControl Solutions
Founded: 1992
United States
www.picontrolsolutions.com/products/pitops/
|
Company InformationDevart
Founded: 1997
Czech Republic
www.devart.com/sdac/
|
|||
Alternatives |
Alternatives |
Alternatives |
Alternatives |
|||
|
|
|
|
||||
|
|
|
|
||||
|
|
|
|||||
|
|
|
|
|
|||
Categories |
Categories |
Categories |
Categories |
|||
Integrations
Azure SQL Database
Delphi
FreeBSD
Python
SQL Server
Ubuntu
|
Integrations
Azure SQL Database
Delphi
FreeBSD
Python
SQL Server
Ubuntu
|
|||||
|
|
|
|
|