Mojo

Mojo

Modular
Pitops

Pitops

PiControl Solutions
blanket.js

blanket.js

Blanket.js

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

Mojo 🔥 — a new programming language for all AI developers. Mojo combines the usability of Python with the performance of C, unlocking unparalleled programmability of AI hardware and extensibility of AI models. Write Python or scale all the way down to the metal. Program the multitude of low-level AI hardware. No C++ or CUDA required. Utilize the full power of the hardware, including multiple cores, vector units, and exotic accelerator units, with the world's most advanced compiler and heterogenous runtime. Achieve performance on par with C++ and CUDA without the complexity.

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

A seamless JavaScript code coverage library. Blanket.js is a code coverage tool for JavaScript that aims to be easy to install, easy to use, and easy to understand. Blanket.js can be run seamlessly or can be customized for your needs. JavaScript code coverage compliments your existing JavaScript tests by adding code coverage statistics (which lines of your source code are covered by your tests). Parsing the code using Esprima and node-falafel, and instrumenting the file by adding code tracking lines. Connecting to hooks in the test runner to output the coverage details after the tests have been completed. A Grunt plugin has been created to allow you to use Blanket like a "traditional" code coverage tool (creating instrumented copies of physical files, as opposed to live-instrumenting). Runs the QUnit-based Blanket report headlessly using PhantomJS. Results are displayed on the console, and the task will cause Grunt to fail if any of your configured coverage thresholds are not met.

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

Companies looking for a solution to design and simulate model predictive controllers

Audience

AI developers interested in a new programming language for AI

Audience

Organizations looking for an advanced process control system that helps identify process dynamics using plant data

Audience

Developers seeking a solution to manage their code tracking processes and statistics

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

$1,180 per year
Free Version
Free Trial

Pricing

Free
Free Version
Free Trial

Pricing

No information available.
Free Version
Free Trial

Pricing

Free
Free Version
Free Trial

Reviews/Ratings

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Overall 0.0 / 5
ease 0.0 / 5
features 0.0 / 5
design 0.0 / 5
support 0.0 / 5

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Reviews/Ratings

Overall 0.0 / 5
ease 0.0 / 5
features 0.0 / 5
design 0.0 / 5
support 0.0 / 5

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Review this Software

Reviews/Ratings

Overall 0.0 / 5
ease 0.0 / 5
features 0.0 / 5
design 0.0 / 5
support 0.0 / 5

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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 Information

MathWorks
United States
www.mathworks.com/products/model-predictive-control.html

Company Information

Modular
Founded: 2022
United States
www.modular.com/mojo

Company Information

PiControl Solutions
Founded: 1992
United States
www.picontrolsolutions.com/products/pitops/

Company Information

Blanket.js
github.com/alex-seville/blanket

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Testwell CTC++

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Categories

Categories

Categories

Categories

Integrations

Axis LMS
JSON
JavaScript
Mocha
Modular
QUnit

Integrations

Axis LMS
JSON
JavaScript
Mocha
Modular
QUnit

Integrations

Axis LMS
JSON
JavaScript
Mocha
Modular
QUnit

Integrations

Axis LMS
JSON
JavaScript
Mocha
Modular
QUnit
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