Robust Online Convex Optimization for Disturbance Rejection

Abstract: This talk will consider robust disturbance rejection in high
precision applications. We will start by motivating the work with one
relevant problem: the control required for optical communication
between satellites. We will then discuss the fundamental performance
limits associated with linear time invariant (LTI) control. Linear
time varying controllers, e.g. those that rely on online convex
optimization, can potentially provide significant performance
improvements. However, the ability to accurately adapt to the
disturbance while maintaining closed-loop stability relies on having
an accurate model of the plant. In fact, the model uncertainty can
cause the closed-loop to become unstable. We provide a sufficient
condition for robust stability based on the small gain theorem using
the ell-infinity norm. This condition is easily incorporated as an
on-line constraint in controllers that rely on online convex
optimization.

Bio: Peter Seiler is an Associate Professor in Electrical Engineering
and Computer Science at the University of Michigan. He is an IEEE
Fellow and the recipient of the O. Hugo Schuck Award (2003) and an NSF
CAREER award (2013). His research focuses on robust control theory
which addresses the impact of model uncertainty on systems design. He
has been a contributor to the Robust Control Toolbox in Matlab since
2001. He was a Principal Scientist from 2004-2008 in the Aerospace
Electronic Systems group at the Honeywell Labs. During that time, he
worked on the redundancy management system for the Boeing 787, sensor
fusion algorithms for automotive active safety systems, and re-entry
flight control laws for NASA’s Orion vehicle.