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Ratings and Reviews 0 Ratings
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What is Model Predictive Control Toolbox?
The Model Predictive Control Toolbox™ provides an extensive array of functions, an easy-to-use app, Simulink® blocks, and useful reference examples to streamline the development of model predictive control (MPC) systems. It effectively addresses linear problems by allowing the development of implicit, explicit, adaptive, and gain-scheduled MPC approaches. For more intricate nonlinear situations, users can implement both single-stage and multi-stage nonlinear MPC. Moreover, this toolbox comes equipped with deployable optimization solvers and allows for the incorporation of custom solvers as needed. Users can evaluate the performance of their controllers through closed-loop simulations within MATLAB® and Simulink environments. In the context of automated driving, the toolbox offers blocks and examples that comply with MISRA C® and ISO 26262 standards, which facilitates the rapid start of projects related to lane keeping assistance, path planning, path following, and adaptive cruise control. It enables the design of implicit, gain-scheduled, and adaptive MPC controllers that can solve quadratic programming (QP) problems while also facilitating the generation of explicit MPC controllers based on implicit designs. Furthermore, the toolbox accommodates discrete control set MPC for addressing mixed-integer QP challenges, thus expanding its versatility for various control systems. With its rich set of features, the toolbox guarantees that both beginners and seasoned professionals can successfully apply advanced control strategies in their projects. This versatility ensures that users across multiple domains can find relevant applications for their specific needs.
What is AMPL?
AMPL is a powerful and intuitive modeling language crafted for articulating and solving complex optimization problems. It empowers users to formulate mathematical models with a syntax akin to algebraic expressions, which facilitates a clear and efficient representation of variables, objectives, and constraints. The language supports a wide array of problem types, encompassing linear programming, nonlinear programming, and mixed-integer programming, among others. One of AMPL's notable strengths lies in its ability to separate models from data, offering both flexibility and scalability for large-scale optimization challenges. Moreover, the platform seamlessly integrates with various solvers, including both commercial and open-source options, allowing users to choose the most appropriate solver for their specific needs. In addition, AMPL is compatible with several operating systems, such as Windows, macOS, and Linux, and offers a variety of licensing options to meet diverse user requirements. This adaptability and user-centric design render AMPL an outstanding option for both individuals and organizations engaged in tackling sophisticated optimization tasks. Its extensive features and capabilities ensure that users are well-equipped to handle a broad spectrum of optimization scenarios.
Integrations Supported
Artelys Knitro
Python
API Availability
Has API
API Availability
Has API
Pricing Information
$1,180 per year
Free Trial Offered?
Free Version
Pricing Information
$3,000 per year
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
MathWorks
Company Location
United States
Company Website
www.mathworks.com/products/model-predictive-control.html
Company Facts
Organization Name
AMPL
Date Founded
2002
Company Location
United States
Company Website
ampl.com/products/ampl/