Ezra Systems Seminar: Kyriakos Vamvoudakis (Georgia Tech)

Description

Also available via Zoom

Learning-based Model-Free Sensor and Actuator Selection in Intelligent Complex Adaptive Systems

Intelligent complex adaptive systems (ICAS) are heterogeneous systems that integrate analog and digital components, along with communication channels through which these components exchange data. Some of the prime components of an ICAS – having a measurable impact on its operational efficiency and productivity – are its sensors and actuators. These are the devices that allow the ICAS to collect data from its environment, as well as to use these data to steer itself toward a desirable direction. Generally speaking, they should be carefully selected to ensure that the system has a good level of observability and controllability, though additional specifications may also be placed depending on the underlying application's specifics. This problem of properly choosing the ICAS' sensors (or actuators) is called the sensor (or actuator) selection problem. In this talk, I will present data driven actuator and sensor selection algorithms, which choose the actuators and sensors of the ICAS while maximizing resiliency. Specifically, model-free learning-based actuator and sensor selection schemes will be proposed to optimize metrics of controllability, observability, and attack resilience for ICAS. I will show how you can use reinforcement learning to select such sensors and actuators with state and output feedback in continuous and discrete-time systems. I will finally present simulation examples with large-scale systems.
 

Bio:
Kyriakos G. Vamvoudakis was born in Athens, Greece. He received the Diploma (a 5-year degree, equivalent to a Master of Science) in electronic and computer engineering from the Technical University of Crete, Greece in 2006 with highest honors. After moving to the United States, he studied at The University of Texas at Arlington with Frank L. Lewis as his advisor, and he received his M.S. and Ph.D. in electrical engineering in 2008 and 2011 respectively. During the period from 2012 to 2016 he was project research scientist at the Center for Control, Dynamical Systems and Computation at the University of California, Santa Barbara. He was an assistant professor at the Kevin T. Crofton Department of Aerospace and Ocean Engineering at Virginia Tech until 2018.

Prof. Vamvoudakis currently serves as the Dutton-Ducoffe Endowed Professor at The Daniel Guggenheim School of Aerospace Engineering at Georgia Tech. His expertise is in reinforcement learning, control theory, game theory, cyber-physical security, bounded rationality, and safe/assured autonomy. Dr. Vamvoudakis is the recipient of a 2019 ARO YIP award, a 2018 NSF CAREER award, a 2018 DoD Minerva Research Initiative Award, a 2021 GT Chapter Sigma Xi Young Faculty Award and his work has been recognized with best paper nominations and several international awards including the 2016 International Neural Network Society Young Investigator (INNS) Award, the Best Paper Award for Autonomous/Unmanned Vehicles at the 27th Army Science Conference in 2010, the Best Presentation Award at the World Congress of Computational Intelligence in 2010, and the Best Researcher Award from the Automation and Robotics Research Institute in 2011.

He currently is a member of the IEEE Control Systems Society Conference Editorial Board, an associate editor of numerous publications, including Automatica; IEEE Transactions on Automatic Control; IEEE Transactions on Neural Networks and Learning Systems; IEEE Computational Intelligence Magazine; IEEE Transactions on Systems, Man, and Cybernetics: Systems; IEEE Transactions on Artificial Intelligence; Neurocomputing; Journal of Optimization Theory and Applications; and of Frontiers in Control Engineering-Adaptive, Robust and Fault Tolerant Control.