Prof. Daniel Quevedo
Queensland University of Technology (QUT), Australia.
Tendrán lugar los días 5, 14 y 19 de octubre de 9:00 a 10:00 a.m.
en la plataforma de Enseñanza Virtual:
https://eu.bbcollab.com/collab/ui/session/guest/f45bacb94a374ac78f05bf3df4b2e6fc
Breve CV del ponente:
Daniel Quevedo received Ingeniero Civil Electrónico and M.Sc. degrees from Universidad Técnica Federico Santa María, Valparaíso, Chile, in 2000, and in 2005 the Ph.D. degree from the University of Newcastle, Australia. He is Professor of Cyberphysical Systems at the School of Electrical Engineering and Robotics, Queensland University of Technology (QUT), in Australia. Before joining QUT, he established and led the Chair in Automatic Control at Paderborn University, Germany.
Prof. Quevedo's research interests are in networked control systems, cyberphysical systems security and control of power converters. He currently serves as Associate Editor for IEEE Control Systems and in the Editorial Board of the International Journal of Robust and Nonlinear Control. From 2015 to 2018 he was Chair of the IEEE Control Systems Society Technical Committee on Networks & Communication Systems.
In 2003 he received the IEEE Conference on Decision and Control Best Student Paper Award and was also a finalist in 2002. Prof. Quevedo is co-recipient of the 2018 IEEE Transactions on Automatic Control George S. Axelby Outstanding Paper Award. He is a Fellow of the IEEE.
Prof. Quevedo's research interests are in networked control systems, cyberphysical systems security and control of power converters. He currently serves as Associate Editor for IEEE Control Systems and in the Editorial Board of the International Journal of Robust and Nonlinear Control. From 2015 to 2018 he was Chair of the IEEE Control Systems Society Technical Committee on Networks & Communication Systems.
In 2003 he received the IEEE Conference on Decision and Control Best Student Paper Award and was also a finalist in 2002. Prof. Quevedo is co-recipient of the 2018 IEEE Transactions on Automatic Control George S. Axelby Outstanding Paper Award. He is a Fellow of the IEEE.
Horarios y resúmenes de las charlas:
5 Octubre, 9:00am
"Predictive Control Methods for Networked Cyber-Physical Systems”
Abstract:
The opportunities provided by feedback control of networked dynamical systems are enormous. Yet it is by no means clear how to harness modern communication, network and computation technologies to obtain high-quality designs. The main stumbling blocks stem from the significant gaps which exist between understanding of constituent parts and the challenges faced when bringing them together. The vast realm of applications of networked cyber-physical systems brings a variety of issues. A common thread is that many of the standard paradigms that allow the separation of computation, communications and systems control are no longer valid. Thus, the need for more holistic approaches arises. This talk illustrates how communication and computation aspects can be integrated into suitable predictive control formulations.
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14 Octubre, 9:00am
"Reinforcement Learning for Networked Estimation and Control”
Abstract:
Cyber–physical systems are systems built through integration of sensors, communication networks, controllers, dynamic (physical) processes and actuators. They are playing an increasingly important role in modern society, in areas such as energy, transportation, manufacturing, and healthcare. The scale of typical CPS such as smart-grids, vehicular traffic networks and smart factories is large. The realisation of these systems faces substantial challenges arising in diverse disciplines, ranging from communications and control to computing
In this talk, we study the scheduling of sensor and actuator data for wireless estimation and control. We focus on setups where, at each time instant, a single agent decides which nodes have access to the network and which ones do not. To address such scheduling problems, we formulate Markov decision processes and solve them using reinforcement learning techniques. The resulting scheduling algorithms can be run online, and are model-free with respect to the wireless channel parameters.
____________________________________________________________________________________________________________
Abstract:
The opportunities provided by feedback control of networked dynamical systems are enormous. Yet it is by no means clear how to harness modern communication, network and computation technologies to obtain high-quality designs. The main stumbling blocks stem from the significant gaps which exist between understanding of constituent parts and the challenges faced when bringing them together. The vast realm of applications of networked cyber-physical systems brings a variety of issues. A common thread is that many of the standard paradigms that allow the separation of computation, communications and systems control are no longer valid. Thus, the need for more holistic approaches arises. This talk illustrates how communication and computation aspects can be integrated into suitable predictive control formulations.
____________________________________________________________________________________________________________
14 Octubre, 9:00am
"Reinforcement Learning for Networked Estimation and Control”
Abstract:
Cyber–physical systems are systems built through integration of sensors, communication networks, controllers, dynamic (physical) processes and actuators. They are playing an increasingly important role in modern society, in areas such as energy, transportation, manufacturing, and healthcare. The scale of typical CPS such as smart-grids, vehicular traffic networks and smart factories is large. The realisation of these systems faces substantial challenges arising in diverse disciplines, ranging from communications and control to computing
In this talk, we study the scheduling of sensor and actuator data for wireless estimation and control. We focus on setups where, at each time instant, a single agent decides which nodes have access to the network and which ones do not. To address such scheduling problems, we formulate Markov decision processes and solve them using reinforcement learning techniques. The resulting scheduling algorithms can be run online, and are model-free with respect to the wireless channel parameters.
____________________________________________________________________________________________________________
19 Octubre, 9:00am
"Remote State Estimation with an Eavesdropper”
Abstract:
We study transmission scheduling for remote state estimation in the presence of an eavesdropper. A sensor transmits local state estimates over a packet dropping link to a remote estimator. At the same time, an eavesdropper can successfully overhear each sensor transmission with a certain probability. The objective is to determine at which instances the sensor should transmit, in order to minimize the estimation error covariance at the remote estimator, while trying to keep the eavesdropper error covariance above a certain level. This is done by solving an optimization problem that minimizes a linear combination of the expected estimation error covariance and the negative of the expected eavesdropper error covariance. Structural results on the optimal transmission policy are presented, and shown to exhibit thresholding behaviour in the estimation error covariances. In the infinite horizon situation, it is shown that with unstable systems one can keep the expected estimation error covariance bounded while the expected eavesdropper error covariance becomes unbounded.