Prof. Makoto IWASAKI (IEEE Fellow, IEEJ Fellow)

Nagoya Institute of Technology, Japan

Biography: Makoto Iwasaki received the B.S., M.S., and Dr. Eng. degrees in electrical and computer engineering from Nagoya Institute of Technology, Nagoya, Japan, in 1986, 1988, and 1991, respectively. He is currently a Professor at the Department of Electrical and Mechanical Engineering, Nagoya Institute of Technology. As professional contributions of the IEEE, he has participated in various organizing services, such as, a Co-Editors-in-Chief for IEEE Transactions on Industrial Electronics since 2016, a Vice President for Planning and Development in term of 2018 to 2021, etc. He is IEEE fellow class 2015 for "contributions to fast and precise positioning in motion controller design".
He has received many academic, foundation, and government awards, like the Best Paper and Technical Awards of IEE Japan, the Nagamori Award, the Ichimura Prize, and the Commendation for Science and Technology by the Japanese Minister of Education, respectively. He is also a fellow of IEE Japan, and a member of Science Council of Japan.
His current research interests are the applications of control theories to linear/nonlinear modeling and precision positioning, through various collaborative research activities with industries.

Prof. Daniel Quevedo (IEEE Fellow)

University of Sydney, Australia

Speech Title: Thompson Sampling for Channel Selection in Networked Estimation and Control

Abstract: The use of machine learning techniques for control has gained increasing attention in recent years. Learning-based estimation and control holds the promise of enabling the solution of problems that are difficult or even intractable using traditional control design techniques. Despite significant progress, several issues, e.g., in relation to stability guarantees, robustness and computational cost, remain. This talk presents some of our recent work on networked control systems with uncertainties and illustrates how posterior sampling techniques can be used for their design. We focus on a basic architecture where sensor measurements and control signals are transmitted over lossy communication channels that introduce random packet dropouts. At any time instant, one out of several available channels can be chosen for transmission. Since channel dropout probabilities are unknown, finding the best channel requires learning from transmission outcomes. We study a scenario where both learning of the channel dropout probabilities and control are carried out simultaneously. Coupling between learning dynamics and control system dynamics raises challenges in relation to stability and performance. To facilitate fast learning we propose to select channels using Bayesian posterior sampling, also called Thompson Sampling. This talk elucidates conditions that guarantee that the resulting system will be stochastically stable and characterises performance in terms of the control regret.

Biography: Daniel E. Quevedo received Ingeniero Civil Electrónico and MSc degrees from Universidad Técnica Federico Santa María, Valparaíso, Chile, in 2000, and in 2005 the PhD degree from the University of Newcastle, Australia. After serving in a number of research intensive roles at Newcastle, in 2015 he relocated to Germany where he established and led the Chair in Automatic Control at Paderborn University. From 2020 to mid-2024, he was the Professor of Cyberphysical Systems with Queensland University of Technology, Australia. He is now a professor in the School of Electrical and Computer Engineering at the University of Sydney.
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 Transactions on Control of Networked Systems, 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. Chenglong Fu

Southern University of Science and Technology, China

Biography: Prof. Chenglong Fu obtained his B.S. from the Department of Mechanical Engineering, Tongji University in 2002 and Ph.D. from the Department of Precision Instruments and Mechanology, Tsinghua University, in 2007. His research interests include dynamic walking, biped and humanoid robots, powered prosthesis, exoskeletons and SuperLimbs. He is the principal investigator of more than 30 research projects. He has published more than 140 papers and holds more than 30 granted patents.  He is an Associate Editor of Robotica (Cambridge University Press, Est. 1983) and workshop co-chair of IEEE-RAS Humanoids 2018-2020; co-organizer in IROS 2019 Workshop (Supernumerary Robotic Limbs); Local Chair, Special Session Chair, and Organizing Chair of IEEE ARM 2020-2023, Associate Editor of IROS 2021-2023; Publicity Committee Co-Chair of ICRA 2021 and Associate Editor of ICRA 2022. He received the Best Student Paper Award Finalist from 2019 IEEE Int. Conf. on Advanced Robotics and its Social Impacts, the Best Student Paper Award from 2020 IEEE Int. Conf. on Advanced Robotics and Mechatronics, the Best Conference Paper Finalist from 2021 IEEE Int. Conf. on Advanced Robotics and Mechatronics, and the Best Paper Award Finalist form 2021 IEEE Int. Conf. on Mechatronics and Machine Vision in Practice. He obtained 2022 First Prize of Shenzhen Science and Technology Progress Award, and 2022 Second prize of Science and Technology Progress Award of Chinese Association of Automation.

Prof. Dan Zhang

Zhejiang University of Technology, China

Biography: Dan Zhang received the B.E. degree in automation and the Ph.D. degree in Control Theory and Control Engineering from the Zhejiang University of Technology, Hangzhou, China, in 2007 and 2013, respectively. He was a Research Fellow with Nanyang Technological University, Singapore, from 2013 to 2014, the National University of Singapore, Singapore, from 2016 to 2017, and the City University of Hong Kong, Hong Kong, from 2017 to 2019. He is now a full professor at the Department of
Automation, Zhejiang University of Technology. Dr. Zhang’s current research interests are in the areas of industrial cyber-physical systems, cooperative control of multi-agent systems, artificial intelligence and fault diagnosis. Dr. Zhang serves as an Associate Editor of ISA Transactions, International Journal of Control, Automation, and Systems, International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, and Cyber-Physical Systems.

Prof. Xingjian JING

City University of Hong Kong

Biography: Prof. JING received his B.S. degree from Zhejiang University, Hangzhou, China, M.S. degree and PhD degree in Robotics from Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, China, respectively. Thereafter, he received a PhD degree in nonlinear systems and signal processing from the Department of Automatic Control and Systems Engineering, University of Sheffield, Sheffield, U.K. His current research interests are generally related to Nonlinear Dynamics, Vibration, and Control focusing on theory and methods for employing nonlinear benefits in engineering, including nonlinear frequency domain methods, nonlinear system identification or signal processing, vibration control, robust control, sensor technology, energy harvesting, nonlinear fault diagnosis or information processing, bio-inspired systems and methods, bio-inspired robotics and control etc.