Grad student sees research in action at Naval Academy

 

 

MechSE PhD student Seungho Lee recently had an incredible opportunity to see his research in action—and not just in the lab. In September, he traveled to the United States Naval Academy in Annapolis, Maryland, to test his model predictive control solution in naval ships on the Chesapeake Bay.

 

Funded by an Air Force Office of Scientific Research (AFOSR) grant, Lee worked with a professor from the Naval Academy to test his findings in action. His AFOSR grant is part of the Multidisciplinary Research University Research Initiatives (MURI) program, a large Illinois-led consortium in collaboration with Stanford University, University of California, Berkeley, Georgia Institute of Technology, and University of Maryland.  

 

Lee’s research is focused on how to solve a game theory problem applied with min-max model predictive control fast enough so the solution can be implemented in real time. This involves developing the algorithm that computes one’s optimal action based on an opponent’s expected movements. The goal is to use the algorithm on full-size Naval Academy ships and demonstrate an effective defense system that can be used in an actual maritime invasion.

 

“We are interested in situations where an intruder tries to outmaneuver and destroy high-value ships in a harbor, and the goal of the ship is to prevent him from doing so. This is called a harbor defense problem. The defense algorithm we’ve developed has received attention from the Naval Academy because it is fast and the quality of the solution is good,” Lee said. “It’s the best/worst case solution. It assumes that the opponent is always doing their best, which puts the defender in a worst-case scenario. If we can find the opponent’s best action, we can make a counteraction and allow our ships to defend the harbor successfully.”

 

Lee is originally from South Korea, where he completed his undergraduate degree. He moved to the U.S. to complete his master’s degree at Texas A&M, and is now working toward his PhD in mechanical engineering, studying model predictive control under Professor Geir Dullerud, and collaborating closely with researchers at UC Berkeley.