March 1, 2023

Monday, March 6 at 4 pm: Add to calendar
Join us in HUB 332 or on Zoom
We will have a great reception afterward!
Recent Advances in Stochastic Control, Network Science, and Ensemble System Theory

In this talk, I will present three of my ongoing research projects. (1) Electric propulsion enables spacecraft to travel farther, faster, and cheaper. However, recent experiments indicate that EP engines experience discharge current fluctuations which are proportional to commanded thrusts. We address this issue from a control-theoretical perspective, and provide a novel guidance law mitigating actuation uncertainty. (2) Graphons, portmanteau of graphs and functions, can be used as stochastic models to sample random graphs. We use graphons to model environment uncertainty and the sampled graphs to represent information flow topologies of network systems operating in the said environment. What is the probability that a random graph sampled from a graphon can sustain a desired system property? We address this question in the asymptotic regime and establish the zero-one property. (3) An ensemble system is, roughly speaking, a large population of parameterized control systems. Driven by emerging applications, there has been an active development in mathematical control theory for analyzing fundamental properties of continuum ensemble systems. I will describe my contributions to this area through the example of quantum spin systems.  

Xudong Chen is an Assistant Professor in the Department of Electrical, Computer, and Energy Engineering at the University of Colorado Boulder. Prior to that, he was a postdoctoral fellow in the Coordinated Science Laboratory at the University of Illinois, Urbana-Champaign. He obtained the B.S. degree in Electronics Engineering from Tsinghua University, China, in 2009, and the Ph.D. degree in Electrical Engineering from Harvard University, Massachusetts, in 2014. He is an awardee of the 2020 Air Force Young Investigator Program, a recipient of the 2021 NSF CAREER award, and the recipient of the 2021 Donald P. Eckman award. His current research interests are in the area of control theory, stochastic processes, optimization, network science, and their applications in large-scale multi-agent systems.