Speaker: Baosen Zhang, Assistant Professor in the Department of Electrical & Computer Engineering at the University of Washington
Title: Optimal Control of Energy Systems via Neural Networks: a Convex Approach
Abstract: In this talk we bridge the gap between model accuracy and control tractability faced by neural networks, by explicitly constructing networks that are convex with respect to their inputs. We show that these input convex networks can be trained to obtain accurate models of complex physical systems. Then optimal controllers can be achieved via solving a convex model predictive control problem. Applications on building HVAC control and distribution system voltage regulation show the promise of our approach compared with existing methods as well as purely data-driven solutions.
Bio: Baosen Zhang is an assistant professor in the Department of Electrical & Computer Engineering at the University of Washington. He received his B.A.Sc. degree in engineering science from the University of Toronto, Toronto, ON, Canada, in 2008 and his Ph.D. from the Department of Electrical Engineering and Computer Science at the University of California at Berkeley, in 2013. Before joining UW, he was postdoctoral scholar at Stanford University, jointly hosted by departments of Civil and Environmental Engineering and Management & Science Engineering. His interest is in the area of power systems and cyberphysical systems, particularly in the fundamentals of physical resource allocations, economics, and controlling systems with humans in the loop.
Seminar Host: Zhaoyu Wang