Modeling the way with academia and industry - Embedded.com

Modeling the way with academia and industry

When I visit companies around the world, I see some recurring themes. The systems they are developing are more complex and perform more functions than ever before. These systems typically include combinations of existing subsystems, off-the-shelf components, and custom subsystems. The development is performed through collaborations of engineering teams representing multiple disciplines, often in different companies or locations around the world.

These companies have found that their traditional development processes are insufficient to address increasing system complexity, the pressure to shorten time-to-market, and customer demands for more functionality with higher quality. As a result, they have modified or completely transformed their development processes to exploit the use of models. They have stopped relying on paper-based specifications, and instead use models as executable specifications that clarify and communicate requirements and specifications. They use multi-domain models to simulate the system-level behavior of their designs. They simulate the subsystems adjacent to their design when the real subsystems are not available or haven’t yet been developed. They automatically generate code for embedded systems from algorithmic models. And they leverage models as test cases and hardware-in-the-loop simulations to test and verify their products and systems. This approach, known as model-based design, is being used in diverse applications, for large and small projects, with co-located and geographically-distributed engineering teams.

These companies are looking to engineering schools to produce engineering graduates with the skills to take full advantage of model-based design. Engineers are asked to think about engineering at a “systems” level rather than only being a specialist in a single domain. They require stronger modeling and analytical skills, not simply an ability to prototype. And, of course, those newly hired engineers must also have a strong foundation in engineering concepts and mathematics.

But large gaps exist between industry needs and engineering education when it comes to modeling. In 2009, the IEEE Control System Society conducted an informal survey of academic and industry members to evaluate the capabilities of engineering graduates. One question asked of the members was, “What areas (if any) need to be strengthened or added to the curriculum to better prepare control engineers for industry?” The responses showed strong consensus across academia and industry about the areas for improvement: hands-on experience, industry-focused design, computer hardware and software, and mathematical modeling of dynamic systems.

The survey also pointed out the discrepancy between what is needed and what is delivered. Over 90% of the industry respondents said that simulation models for system verification or product design, nonlinear models, real-time models for hardware-in-the-loop verification, and experimental system identification methods are useful and valuable skills for controls engineers in industry. Yet, less than 50% of the university respondents said that those are “topics covered in a course or courses that you regularly or occasionally teach, and that would typically be completed by entry-level control engineers graduating from your institution.”

Some universities have taken significant steps to expose engineering students to modeling and simulation techniques, particularly in controls, signal processing, and mechatronics labs. Senior-year design projects are increasingly team-based, not individual, and frequently involve building and sharing models. GM, the primary sponsor of the ChallengeX and EcoCAR student competitions for fuel-efficient vehicle design, considers modeling and analysis to be so important in its own processes that it required the student teams to model, simulate, and analyze their design extensively for a year of the three-year competition before even having access to vehicles.

About the author
Jim Tung has more than 25 years experience in the technical computing software markets. He is a 20-year veteran of The MathWorks, holding the positions of vice president of marketing and vice president of business development before assuming his current role focusing on business and technology strategy and analysis. Jim holds a bachelor's degree from Harvard University.

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