Average Ratings 0 Ratings
Average Ratings 0 Ratings
Description
The Model Predictive Control Toolbox™ offers a comprehensive suite of functions, an intuitive app, Simulink® blocks, and practical reference examples to facilitate the development of model predictive control (MPC) systems. It caters to linear challenges by enabling the creation of implicit, explicit, adaptive, and gain-scheduled MPC strategies. For more complex nonlinear scenarios, users can execute both single-stage and multi-stage nonlinear MPC. Additionally, this toolbox includes deployable optimization solvers and permits the integration of custom solvers. Users can assess the effectiveness of their controllers through closed-loop simulations in MATLAB® and Simulink environments. For applications in automated driving, the toolbox also features MISRA C®- and ISO 26262-compliant blocks and examples, allowing for a swift initiation of projects related to lane keep assist, path planning, path following, and adaptive cruise control. You have the capability to design implicit, gain-scheduled, and adaptive MPC controllers that tackle quadratic programming (QP) problems, and you can generate an explicit MPC controller derived from an implicit design. Furthermore, the toolbox supports discrete control set MPC for handling mixed-integer QP challenges, thus broadening its applicability in diverse control systems. With these extensive features, the toolbox ensures that both novice and experienced users can effectively implement advanced control strategies.
Description
The QMSys GUM Software is designed for assessing the uncertainty inherent in physical measurements, chemical analyses, and calibration processes. It employs three distinct methodologies to compute measurement uncertainty. The first, GUF Method for linear models, targets linear and quasi-linear models, aligning with the GUM Uncertainty Framework. This approach calculates partial derivatives, representing the initial terms of a Taylor series, to ascertain sensitivity coefficients for the equivalent linear model, followed by the determination of combined standard uncertainty using the Gaussian error propagation law. The second, GUF Method for nonlinear models, caters to nonlinear models where results exhibit symmetric distribution. This method incorporates various numerical techniques, including nonlinear sensitivity analysis and higher-order sensitivity indices, as well as quasi-Monte Carlo simulations utilizing Sobol sequences. With its multifaceted approach, the software provides comprehensive tools for uncertainty analysis across different measurement contexts.
API Access
Has API
No
API Access
Has API
No
Integrations
No details available.
Integrations
No details available.
Pricing Details
$1,180 per year
Free Trial
Yes
Free Version
No
Pricing Details
No price information available.
Free Trial
Yes
Free Version
No
Deployment
Web-Based
No
On-Premises
No
iPhone App
No
iPad App
No
Android App
No
Windows
Yes
Mac
Yes
Linux
Yes
Chromebook
No
Deployment
Web-Based
No
On-Premises
No
iPhone App
No
iPad App
No
Android App
No
Windows
Yes
Mac
No
Linux
No
Chromebook
No
Customer Support
Business Hours
Yes
Live Rep (24/7)
No
Online Support
Yes
Customer Support
Business Hours
Yes
Live Rep (24/7)
No
Online Support
Yes
Types of Training
Training Docs
Yes
Webinars
Yes
Live Training (Online)
No
In Person
Yes
Types of Training
Training Docs
Yes
Webinars
No
Live Training (Online)
No
In Person
Yes
Vendor Details
Company Name
MathWorks
Country
United States
Website
www.mathworks.com/products/model-predictive-control.html
Vendor Details
Company Name
Qualisyst
Founded
1994
Country
Bulgaria
Website
www.qsyst.com/qualisyst_en.htm
Product Features
Product Features
Statistical Analysis
Analytics
Yes
Association Discovery
Yes
Compliance Tracking
Yes
File Management
No
File Storage
Yes
Forecasting
No
Multivariate Analysis
Yes
Regression Analysis
Yes
Statistical Process Control
No
Statistical Simulation
Yes
Survival Analysis
No
Time Series
No
Visualization
Yes