Abstract:

Physical sensing and control often involves switching in the governing equations. For instance, skid-steered vehicles must violate nonholonomic constraints in order to maneuver. This sliding of the wheel against the ground causes the vehicle to behave discontinuously during a maneuver as well as making the vehicle's state difficult to estimate. Estimation of contact state is most naturally framed in the context of switched systems, where the vehicle's ground interaction is modeled by partitioning the system dynamics into distinct modes of behavior. Thus, as the vehicle maneuvers, the system evolves over some mode sequence, transitioning between modes over some set of switching times. A hybrid form of the maximum principle can be used to pose the estimation process as a hybrid optimization over the space of all possible modal behaviors.  Somewhat surprisingly, this combinatoric estimation problem can be represented as a projection of an infinite-dimensional optimization problem that can be approximated by relaxing the projection. Using this approach, both first-order and second-order optimization techniques can be employed, even in the presence of significant noise. Moreover, because of the quadratic convergence associated with second-order methods, one can implement these techniques in real-time settings.

Biography:

Todd Murphey received his undergraduate degree in mathematics from the University of Arizona and a Ph.D. in Control and Dynamical Systems from the California Institute of Technology. He was a postdoctoral scholar at Northwestern University for a year, after which he worked for the Aerospace Corporation in the Electro-Mechanical Control Department. He was an Assistant Professor of Electrical and Computer Engineering at the University of Colorado at Boulder from 2004 to 2008 and is now an Assistant Professor of Mechanical Engineering at Northwestern University. He is the recipient of a National Science Foundation CAREER award.