A better-developed Boeing workforce leads to many benefits for the company, its customers – and society
Change comes at a rapid pace.
We can see on the horizon a new age of autonomous systems for which opportunities are endless: Self driving cars and trucks, autonomous package and cargo delivery systems, medical diagnostics and operating room robotics.
Autonomous systems with machine reasoning and intelligence will change forever how machines serve humans. Humans and machines will both understand mission context, sharing understanding and situational awareness, and adapt to the needs and capabilities of each other. Sensor networks will provide unprecedented safety, reliability, communication and performance.
For our workforce to compete and be successful in the future, they will need to learn fast to keep up. Our success will be fueled by our ability to learn new technology skills, new applications, and new ways to conceive of problem-solving in light of machine learning and autonomy.
So how will we teach our new generation of engineers? Many Boeing technical fellows are engaged in teaching post-graduate level courses at colleges and universities.
I have the privilege of teaching graduate level control theory at Washington University in Saint Louis, home to one of 122 engineering Grand Challenge schools, colleges that are committed to educating a new generation of engineers expressly equipped to tackle some of the most pressing issues facing society in the 21st century.
Control theory brings together advanced mathematics, modeling and simulation, software development, and forms the kernel for the operating systems embedded within an autonomous system.
Using some of the most recent and challenging aerospace problems in my courses teaches engineers how far technology has come, how far it needs to go, and what skills will be needed in the future. Our recent text book on robust and adaptive control theory is in the top three best sellers for the publisher. It captures the most useful and successful control theories used in aerospace, and builds upon them to create adaptive system theory needed for future self-learning autonomous systems.
One of the most significant challenges in autonomous system development is dealing with failures, damage, or when things go wrong. This is called contingency management. It is impossible (and cost prohibitive) to program conventional If-Then-Else control logic for every possible scenario. Our method uses model reference adaptive control, in which a reference model is used to model and produce healthy system behaviors. During a contingency event, if the system differs from the model’s response, a neural-based nonlinear control algorithm forces the system to behave like the model, providing both stability and performance. Our algorithms don’t need to know what went wrong, which makes the design robust. They are deterministic, repeatable and testable, which is critical for Boeing products.
All of our early career engineers who work in flight control take my courses, which are now being taught at Boeing for credit and can use real Boeing challenges to teach and demonstrate the theory. This advanced leaning solidifies the use of fundamentals, and accelerates our engineers’ skill development to make them ready for our future challenges.
Preparing our engineering workforce for the future is one of the most important duties for an industry leader like Boeing. Our engineers are eager to run in this technology race. The excitement they bring will make the world a better place over the next 100 years.
By Kevin Wise, Senior Technical Fellow