DCL Special Seminar: Anders Rantzer - Adaptive Control - What Can We Learn?

Anders Rantzer, Ph.D.
Professor and Head of Department
Lund University

Monday, April 1, 2019
11am – 12noon
141 Coordinated Science Laboratory

Adaptive Control - What Can We Learn?

Abstract:
The history of adaptive control dates back to aircraft autopilot
development in the 1950s. Computer control and system identification
lead to a surge of research activity during the decades to follow.
Recently, research activities have started to grow again, for reasons
similar to the growth of machine leaning; abundance of data and
computing resources creates an ever-growing stream of engineering
opportunities for adaptation. This presentation will discuss some new
research directions for theory of adaptive control. The first one is
stimulated by the rapid progress in statistical learning theory for tail
and concentration bounds, which makes it possible to analyse transient
properties of adaptive controllers in a rigorous manner. The second
direction is based on deterministic analysis of worst case response to
disturbances. By studying min-max games like in H-infinity control, it
is possible to compute adaptive feedback controllers with an optimal
exploration-exploitation trade-off.

Bio:
Anders Rantzer received a PhD in 1991 from KTH, Stockholm, Sweden. After postdoctoral positions at KTH and at IMA, University of Minnesota, he joined Lund University in 1993 and was appointed professor of Automatic Control in 1999. The academic year of 2004/05 he was visiting associate faculty member at Caltech and 2015/16 he was Taylor Family Distinguished Visiting Professor at University of Minnesota. During 2008-18 he coordinated the Linnaeus center LCCC at Lund University and he currently serves as head of department. He is a Fellow of IEEE, a member of the Royal Swedish Academy of Engineering Sciences and past chairman of the Swedish Scientific Council for Natural and Engineering Sciences. His research interests are in modeling, analysis and synthesis of control systems, with particular attention to uncertainty, optimization, scalability and adaptation.