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ease
features
design
support

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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

NLREG is an advanced statistical analysis tool designed for both linear and nonlinear regression analysis, as well as for fitting curves and surfaces. It identifies the optimal values of parameters for a user-defined equation, ensuring that it best aligns with a given set of data points. Capable of managing various function types, including linear, polynomial, exponential, logistic, periodic, and more general nonlinear forms, NLREG stands out because it can accommodate nearly any algebraically specified function. Unlike many other nonlinear regression tools that are restricted to a limited selection of functions, NLREG offers a comprehensive range of possibilities. The program incorporates a robust programming language with a syntax akin to C, allowing users to define the function to be fitted while enabling the computation of intermediate variables, the use of conditionals, and the implementation of iterative loops. Furthermore, NLREG simplifies the creation of piecewise functions that can adapt their form across different ranges. Additionally, the inclusion of arrays in the NLREG language facilitates the use of tabular lookup methods to designate the function, providing even greater flexibility for users in their analyses. Overall, NLREG is an invaluable asset for statisticians and data analysts seeking to conduct complex fitting tasks.

API Access

Has API

API Access

Has API

Screenshots View All

Screenshots View All

Integrations

No details available.

Integrations

No details available.

Pricing Details

$1,180 per year
Free Trial
Free Version

Pricing Details

No price information available.
Free Trial
Free Version

Deployment

Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook

Deployment

Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook

Customer Support

Business Hours
Live Rep (24/7)
Online Support

Customer Support

Business Hours
Live Rep (24/7)
Online Support

Types of Training

Training Docs
Webinars
Live Training (Online)
In Person

Types of Training

Training Docs
Webinars
Live Training (Online)
In Person

Vendor Details

Company Name

MathWorks

Country

United States

Website

www.mathworks.com/products/model-predictive-control.html

Vendor Details

Company Name

NLREG

Website

www.nlreg.com

Product Features

Statistical Analysis

Analytics
Association Discovery
Compliance Tracking
File Management
File Storage
Forecasting
Multivariate Analysis
Regression Analysis
Statistical Process Control
Statistical Simulation
Survival Analysis
Time Series
Visualization

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