Abstract
Model Predictive Control (MPC) leads to algorithmically defined nonlinear feedback laws for systems with pointwise-in-time state and control constraints. These feedback laws are defined by solutions to appropriately posed dynamic optimization problems that are (typically) solved online. To enable MPC implementation, the solutions to MPC optimization problems must be computed reliably and within the available time.
The talk will reflect on recent research by the presenter and his students/collaborators into strategies for computing solutions to MPC optimization problems. These strategies include Newton-Kantorovich methods for solving MPC problems inexactly and KS functions-based constraint aggregation strategies for handling systems with multiple constraints. The development of add-on supervisory schemes for MPC which reduce the computational time and enlarge the constrained closed-loop region of attraction will be also discussed. In particular, a Computational Governor (CG) will be described which maintains feasibility and bounds the suboptimality of the MPC warm-start by alternating the reference command provided to the inexactly solved MPC problem.
The talk will end by touching upon the integration of game theoretic models into model predictive control to facilitate decision-making in dynamic and interactive environments including self-driving cars operating in traffic and performing forced merging and intersection crossing maneuvers.
Biography
Professor Ilya V. Kolmanovsky has received his Ph.D. degree in Aerospace Engineering in 1995, his M.S. degree in Aerospace Engineering in 1993 and his M.A. degree in Mathematics in 1995, all from the University of Michigan, Ann Arbor. He is presently a full professor in the Department of Aerospace Engineering at the University of Michigan. Professor Kolmanovsky’s research interests are in control theory for systems with state and control constraints, and in control applications to aerospace and automotive systems. Before joining the University of Michigan in January 2010, he was with Ford Research and Advanced Engineering in Dearborn, Michigan for close to 15 years. He is a Fellow of IEEE and IFAC, an Associate Fellow of AIAA, a past recipient of the Donald P. Eckman Award of American Automatic Control Council, of 2002 and 2016 IEEE Transactions on Control Systems Technology Outstanding Paper Awards, of SICE Technology Award, of several technical achievement, innovation and publication awards of Ford Research and Advanced Engineering. His publication record includes over 200 journal articles, over 400 conference papers, over 20 book chapters, 4 edited books, as well as 104 United States patents. He serves as a Senior Editor for IEEE Transactions on Control Systems Technology.
