In June of this year I had the opportunity to attend our DISTek-led Modeling and Simulation training course. Currently, I am the lead of the Automation and Test group here at DISTek, but in my previous roles I have been heavily involved in desktop application development, as well as test system development primarily using LabVIEW. My exposure to modeling and embedded has been somewhat limited thus far, so I thought it would be a good experience to learn some of the basics.
In the last phase of DISTek’s 25 years, we expanded our engineering services again. The concept of model-based software development (MBSD) had been in use for a few years at several of our off-highway customers and we had gained experience with it. But as we surveyed the market landscape........
Engineers working in the off-highway vehicle equipment industry, particularly with electronics.
This training is designed as an introduction into MBSD, specifically around the toolboxes MATLAB, Simulink, and Stateflow. To start, this class is meant to be an alternative to the 5 day training class that the Mathworks offers. The class will be run leaner with a greater focus on MBSD. DISTek will be incorporating feedback from attendees to make sure there are no significant gaps. The goal of this class is to provide a basic introduction to Simulink and Stateflow block sets, applying them to implement basic features. The class will be hands on with demos and examples throughout, ending with more specific in-depth exercises. Best practices and MAAB guidelines will be employed throughout the training.
Recently my team was given the opportunity to completely redo a particularly messy and troublesome piece of legacy C code, and as a team we decided to give MBSD a try. We had tried a few simple models before, all of which turned out to be more complicated than had we written the C code ourselves. But this time we were determined to do it right: we allocated plenty of time, received one-on-one training from the local MBSD guru, and reviewed the original requirements to ensure they met the needs of the system. Finally after exhausting all of the time, continually pestering the guru with questions and modifying the requirements several times, we succeeded in having a model based software design that actually worked the first time we tested it on the vehicle. It was a valuable experience overall and helped illustrate the drawbacks and benefits of MBSD over the typical C development.Read full blog post here...
When I was attending classes at Northern Illinois University in pursuit of a Computer Science degree, I enrolled in a class called Software Engineering. The general idea of this class was training in the ability to elicit requirements from someone without a knowledge of how software works or how it should be created. The thinking was that someone with a better understanding of how a system should work would be led into providing the guidelines for the implementation so that the software developer could create their vision. But what if there was a way for that person to directly transfer their ideas into software, leaving the developer free to work with lower levels of implementation and the real nitty-gritty of the development?
For software engineers who use Simulink to do Model Based Software Development (MBSD), the ability to manage variables, also known as signals and parameters in Simulink, has always been a challenge. It has been possible but not without shortcomings, especially when dealing with large software systems.
For those not familiar with Simulink, Here is a brief outline of what the past issues have been.
We were asked to assist in the development of the controls for a complex construction machine using Model Based Software Design (MBSD). This was the first project in which the OEM used MBSD and therefore it was a high-visibility project.
We faced many challenges in this project. The control algorithm was very complex, and had already been developed by the OEM in Matlab - the accuracy of the implementation of the algorithm was critical. Verification of the algorithm was a key component of this project. Adding to the complexity, the input signals to the system were very noisy and required disturbance rejection. Any instabilities in the system had the potential to cause injuries or property damage, so it was key to tune the system properly. Requirements for the project were also being developed in parallel to implementation.