Autonomous technology will touch nearly every part of our lives, changing the products we build and the way we do business. It’s not just in self-driving cars, robots, and drones; it’s in applications like predictive engine maintenance, automated trading, and medical image interpretation. Autonomy—the ability of a system to learn to operate independently—requires three elements:
In this talk, Chris Hayhurst shows you how engineers and scientists are combining these elements, using MATLAB® and Simulink®, to build autonomous technology into their products and services today—to build their autonomous anything.
Grid Solutions, a GE and Alstom joint venture, helps power utilities to bring power reliably and efficiently from the point of generation to the end user (power consumers). One of the product lines is FACTS (Flexible AC Transmission Systems), which focuses on power transmission. Control of reactive power is used as the means to improve the stability and capacity of electrical grids. Using sophisticated algorithms with very high sample rates, control systems calculate optimal conditions to meet demanding performance requirements. In achieving this, and while constantly looking for improvements, Grid Solutions saw the potential of using Model-Based Design in the design process. This presentation gives an overview on the adoption of Model-Based Design at Grid Solutions and shows how it is used in reactive power compensation systems.
Ponsse is a forest machine company concentrating on sales, service, manufacturing, and technology related to cut-to-length forest machines. Ponsse invests substantially in product development and has close to 100 industry professionals focusing on technological research and development.
This presentation shows how Ponsse uses Model-Based Design including rapid prototyping to address the challenges arising from quickly growing requirements and the complexity of software algorithms for machine control systems. The challenges lead to increasing amounts of software, which is usually hard both to understand and maintain. Using simulation and real-time testing environment offers Ponsse a way to develop new complex functionalities for forest machines faster and with fewer errors.
Axiomatic Technologies Corporation designs and manufactures electronic controllers for OEM use. One goal is to keep the software design environment as open and flexible as possible. Simulink® and Stateflow®, together with few other essential tools, give users the freedom to collaborate with OEMs on the controller design procedure without the need for C programming.
The Visctronic Device Controller was developed in collaboration with BorgWarner Thermal Systems. BorgWarner Thermal Systems, part of the BorgWarner Engine Group, provides engine thermal management components for global vehicle manufacturers and aftermarket applications.
This session shows how the use of Simulink and Model-Based Design technology made it possible to share design tasks between two groups of engineers. The task for Axiomatic engineers was to provide a proper set of Simulink blocks and S-functions for accessing all features on the visctronic device controller board, while the actual, very advanced visctronic device control algorithm development was the responsibility of BorgWarner’s engineers. This solution made the task of designing the overall system straightforward and successful for both parties.
Verification and validation techniques applied throughout the development process enable you to find errors before they can derail your project. Most system design errors are introduced in the original specification, but are not found until the test phase. Learn how you can apply MATLAB® and Simulink® verification and validation products throughout your development and certification process to find bugs early and reduce development time and effort.
Companies that make industrial equipment are storing large amounts of machine data, with the notion that they will be able to extract value from it in the future. However, using this data to build accurate and robust models that can be used for prediction requires a rare combination of equipment expertise and statistical know-how.
In this presentation, we discuss machine learning techniques in MATLAB® to estimate remaining useful life of equipment. Using a real-world example, we show how MATLAB is used to build prognostics algorithms and take them into production, enabling companies to improve the reliability of their equipment and build new predictive maintenance services.