Modern society is critically dependent on large-scale networks for services such as energy supply, transportation and communications. The design and control of such networks is becoming increasingly complex, due to their growing size, heterogeneity and autonomy. This workshop brings together thought-leaders in the field to discuss future directions in scalable system theory, architectures and incentives, and learning.
Florian Dorfler
ETH Zurich
Nikolai Matni
University of Pennsylvania
Fernando Paganini
Universidad ORT
Rodolphe Sepulchre
KU Leuven
Maria Elena Valcher
University of Padova
Kaoru Yamamoto
Kyushu University
Munther Dahleh
MIT
John C. Doyle
Caltech
Karl J. Åström
Lund University
Anders Lindqvist
Anhui University
Welcome
Workshop organizers
Opening remarks
Karl J. Åström, Lund University
Relaxation is all you need
Rodolphe Sepulchre, KU Leuven
Intervention and causality
Munther Dahleh, MIT
Coffee break
Data-driven methods for state estimation in the presence of disturbances: current results and future perspectives
Maria Elena Valcher, University of Padova
LQR learning pipelines
Florian Dorfler, ETH Zurich
Lunch break
Representation learning for dynamics and control
Nikolai Matni, University of Pennsylvania
Selfish routing in distributed EV charging networks through dynamics and convex optimization
Fernando Paganini, Universidad ORT
Coffee break
Optimising the intersample behaviour for multi-robot systems
Kaoru Yamamoto, Kyushu University
Bildung, control, and architecture at Lund
John C. Doyle, Caltech
Closing remarks
Anders Lindqvist, Shanghai Jiaotong
Mikael Johansson
KTH
Henrik Sandberg
KTH
Karl H. Johansson
KTH
Tryphon Georgiou
UC Irvine