The technical program will be available in early February, 2021.
Abstract: In this presentation, we will introduce the maximum hands-off control, also known as L0-optimal control. Maximum hands-off control minimizes the length of the support (i.e. the L0 norm) of the control among all feasible controls. We will see that the L0-optimal control is equivalent to the classical L1-optimal control (also known as minimum-fuel control) under a mild assumption. We also show extension of maximum hands-off control to discrete-valued control and distributed control.
Biography: Masaaki Nagahara received the bachelor's degree in engineering from Kobe University in 1998, and the master's degree and the Doctoral degree in informatics from Kyoto University in 2000 and 2003, respectively. He is currently a Full Professor with the Institute of Environmental Science and Technology, The University of Kitakyushu. He has also been a Visiting Professor with Indian Institute of Technology Bombay since 2017. His research interests include optimal control, machine learning, and networked systems. He received George S. Axelby Outstanding Paper Award in 2018 and Transition to Practice Award in 2012 from IEEE Control Systems Society. He also received SICE Control Division Research Award (Hidenori Kimura Award) in 2020, Best Book Authors Award in 2016, Best Paper Award in 2012, and Young Authors Award in 1999 from SICE, and Best Tutorial Paper Award in 2014 from IEICE Communications Society. He is a senior member of IEEE, and a member of SICE, ISCIE, JSME, and IEICE.
Abstract: Standard stochastic control methods assume that the probability distribution of uncertain variables is available. Unfortunately, in practice, obtaining accurate distribution information is a challenging task. To resolve this issue, we investigate the problem of designing a controller that is robust against errors in the empirical distribution obtained from data. The proposed framework using the Wasserstein metric has several salient features, including an out-of-sample performance guarantee, an explicit solution in the LQ setting, and a theoretical connection to the classical H_infty method. We further discuss its MPC variant and application to motion planning and control in risky environments.
Biography: Dr. Insoon Yang is an Associate Professor of Electrical and Computer Engineering at Seoul National University. He received B.S. degrees in Mathematics and in Mechanical Engineering (summa cum laude) from Seoul National University in 2009; and an M.S. in EECS, an M.A. in Mathematics and a Ph.D. in EECS from UC Berkeley in 2012, 2013 and 2015, respectively. He was an Assistant Professor of Electrical and Computer Engineering at University of Southern California from 2016 to 2018, and a Postdoctoral Associate with the Laboratory for Information and Decision Systems at Massachusetts Institute of Technology from 2015 to 2016. His research interests are in stochastic control and optimization, and reinforcement learning, with application to cyber-physical systems and safe autonomy. He is a recipient of the 2015 Eli Jury Award and a finalist for the Best Student Paper Award at the 55th IEEE Conference on Decision and Control 2016. He is an associate editor of the IEEE CSS Conference Editorial Board and a vice-chair of the IFAC Stochastic Systems Technical Committee.