Prof. Hajime Asama
Department of Precision Engineering, The University of Tokyo, Japan
(President of IFAC; Fellow of IEEE, JSME, and RSJ)

Abstract: Recently, the frequency and the severity of the disaster are increasing due to global warming and aging of the social infrastructures. In the disaster sites, there are difficulty and danger in tasks and environment for human workers, and it is necessary to utilize the robot technology for disaster response.
In this talk, the robot technologies which have been developed and utilized for the disaster response including decommissioning of the Fukushima Daiichi Nuclear Power Station are introduced, and new challenges for disaster response robot technology and issues on its societal dissemination are discussed.

Biography: Hajime Asama received his B.S., M.S., and Dr. Eng in Engineering from the University of Tokyo, in 1982, 1984 and 1989, respectively. He worked at RIKEN (Institute of Physical and Chemical Research) in Japan from 1986 to 2002 as a research scientist, etc. He became a professor of RACE (Research into Artifacts, Center for Engineering) of the University of Tokyo in 2002, and a professor of School of Engineering of the University of Tokyo since 2009.

He received JSME (The Japan Society of Mechanical Engineers) Robotics and Mechatronics Academic Achievement Award in 2001, JSME Funai Award in 2009, JSME Robotics and Mechatronics Award in 2009, SICE (The Society of Instrument and Control Engineers) System Integration Division System Integration Award for Academic Achievement in 2010, RSJ (Robotics Society of Japan) Distinguished Service Award in 2013, JSME Award (Technical Achievement) in 2018, etc.

He was the vice-president of RSJ in 2011-2012, an AdCom (Administrative Committee) member of IEEE (The Institute of Electrical and Electronics Engineers) Robotics and Automation Society in 2007-2009. Currently, he is the president-elect of IFAC since 2017, the president of International Society for Intelligent Autonomous Systems since 2014, and an associate editor of Control Engineering Practice, Journal of Robotics and Autonomous Systems, and Journal of Field Robotics, etc. He played the director of the Mobiligence (Emergence of adaptive motor function through the body, brain and environment) program in the MEXT (Ministry of Education, Culture, Sports, Science and Technology) Grant-in-Aid for Scientific Research on Priority Areas from 2005 to 2009. He is a member of Science Council of Japan from 2014 to 2017, and a council member since 2017. He is a Fellow of IEEE, JSME and RSJ.

He was a member of Decommissioning Strategy Committee of NDF (Nuclear Damage Compensation and Decommissioning Facilitation Corporation) from 2014 to 2016, and is a member of Fuel Debris Retrieval Expert Committee and a member of Decommissioning R&D Partnership Council of NDF since 2014 and 2015 respectively. He is also a member of technical committee of IRID (International Research Institute for Nuclear Decommissioning), a member of technical committee on mockup testing facility of JAEA (Japan Atomic Energy Agency), the project leader on Disaster Response Robots of COCN (The Council on Competitiveness-Japan), etc.
His research interests are service robotics, distributed autonomous robotic systems, ambient intelligence, rescue robotics, Mobiligence, embodied-brain systems, service engineering, human interface, etc. He has been involved in activities to utilize and disseminate robot technology in decommissioning of Fukushima Daiichi Nuclear Power Station, disaster response, inspection of social infrastructures, and for Fukushima Innovation Coast Framework.

Prof. Robert Bitmead
Dept. of Mechanical and Aerospace Engineering, University of California at San Diego, USA
(Fellow of IEEE, IFAC, and AATSE)

Abstract: Starting from a puzzle posed by observability in linear systems with additive noise, we consider the definition and role of observability in Kalman filtering style state estimation problems. Observability evidently is a non-Boolean aspect of a system; there are some systems which are more observable than others. These ideas are then pushed into the domain of nonlinear systems where state and parameter estimation and identification merge. In this more general context, observability and identifiability ideas coalesce and are related not just to system properties or parametrizations but also to signal properties and questions of excitation. An example in telecommunications congestion control illustrates the excitation needs of real-time estimation algorithms. This excitation comes with an apparent cost to performance but, without it, the system fails. This arrives at two themes: on-line estimation and adaptation come with excitation costs, and in considering contemporary questions of so-called Big Data, big is not necessarily beautiful. It would be preferable to have a modest quantity of high information-content data. How might one think about doing this?

Biography: Bob Bitmead is Distinguished Professor at the University of California, San Diego. He holds degrees in Applied Mathematics from Sydney University and Electrical Engineering from Newcastle University. He has held faculty positions at the Australian National University and James Cook University of North Queensland. He is a control theorist with a long experience in control applications in many industrial sectors. His theoretical work is strongly informed and guided by these applications. He was the recipient of the 2014 ASME Rufus Oldenburger Medal and of the 2015 IEEE Controls Systems Transition to Practice Award. Bob was President of the IEEE Control Systems Society in 2019. He was a member of the IFAC Council from 1996 to 2002 and is the founding Editor-in-Chief of the IFAC Journal of Systems & Control. He is General Chair of the 2025 IEEE Conference on Control Technology & Applications in San Diego. He is Fellow of IEEE, IFAC and the Australian Academy of Technological Sciences and Engineering. Bob brews his own beer and is an accredited and active Australian Rules Football umpire.

Prof. Lei Guo
Academy of Mathematics and Systems Science, Chinese Academy of Science
(Fellow of IEEE, IFAC, and TWAS; IEEE Bode Lecture Prize)

Abstract: In this lecture, a survey of some theoretical progresses on several basic problems in systems and control science will be presented. We will begin by discussing the following three problems that are related to the emergence of complex systems: How cooperation arises from rational players? When flocks with large population will be synchronized? Can a general theory for distributed adaptive filtering be established? Then we will focus on the understanding of the feedback mechanism for regulating uncertain dynamical systems and to answer the following questions: What are the main features of adaptive control systems where online estimators are combined with real-time controllers in the same feedback loop? Can we establish the global stability and optimality of the well-known self-tuning regulators? What is the rationale behind the widespread use of the classical proportional-integral-derivative (PID) control? Can a global theory and design method be provided for uncertain nonlinear systems? How about the maximum capability and fundamental limitations of the feedback mechanism in dealing with nonlinear uncertain systems? Finally, we will discuss how a theory may be established for game-based control systems (GBCS), where some “intelligent” behaviors including dynamical games may exist in the systems to be regulated.

Biography: Lei GUO received his B.S. degree in mathematics from Shandong University in 1982, and Ph.D. degree in control theory from the Chinese Academy of Sciences in 1987. He was a postdoctoral fellow at the Australian National University (1987-1989). Since 1992, he has been a Professor of the Institute of Systems Science at the Chinese Academy of Sciences (CAS), where he had been Director of the Institute (1999-2002). From 2002 to 2012，he was the President of the Academy of Mathematics and Systems Science, CAS. He is currently the Director of the National Center for Mathematics and Interdisciplinary Sciences, CAS.

Dr. Guo was elected Fellow of the IEEE in 1998, Member of the Chinese Academy of Sciences in 2001, Fellow of the Academy of Sciences for the Developing World (TWAS) in 2002, Foreign Member of the Royal Swedish Academy of Engineering Sciences in 2007, and Fellow of the International Federation of Automatic Control (IFAC) in 2007 “for fundamental contributions to the theory of adaptive control and estimation of stochastic systems, and to the understanding of the maximum capability of feedback”. In 2014, he was awarded an honorary doctorate by Royal Institute of Technology (KTH), Sweden. His coauthored paper on synchronization of flocks was selected and published as a SIGEST Paper by SIAM Review in 2014. He was also the recipient of the 1993 IFAC World Congress Young Author Prize “for solving a long standing problem in control theory concerning convergence and convergence rate for the least-squares–based self-tuning regulators”. He was awarded the 2019 Hendrik W.Bode Lecture Prize by the IEEE Control Systems Society. He has delivered twice plenary lectures at the IFAC World Congress in 1999 and 2014 respectively, and was an Invited speaker at the International Congress of Mathematicians (ICM) in 2002. He has also been selected as a distinguished lecturer of the IEEE Control Systems Society (2012-2014).

He has served as a Council Member of IFAC (2005-2011), Member of IEEE Control Systems Award Committee (2008-2011, 2018-2019), Member of the IFAC Award Committee (2005-2008, 2017-2020), Associate Editor of SIAM J. Control and Optimization (1991-1993), General Co-Chair of the 48th IEEE Conference on Decision and Control (2009), Deputy Director of the CAS Academic Committee (2013-2018), Congress Director of the 8th International Congress on Industrial and Applied Mathematics (ICIAM 2015), Advisor of the National Basic Research Program of China (2011-), President of the China Society for Industrial and Applied Mathematics (CSIAM), Vice-President of the Chinese Mathematical Society, and Vice-President of the Chinese Association of Automation. Currently, he serves as the Director of the Information Science Division of CAS Academic Committee (2018-), Deputy Director of the Division of Information Technical Sciences, CAS (2016-), and a member of editorial boards of a number of academic journals in mathematics, systems and control.

He has worked on problems in adaptive control, system identification, adaptive signal processing, and stochastic systems. His current research interests include control of nonlinear uncertain systems, PID control theory, distributed filtering and estimation, capability of feedback, multi-agent systems, game-based control systems, and complex systems, among others.

Prof. Shuzhi Sam Ge
Dept. of Electrical and Computer Engineering, The National Univ. of Singapore
(Fellow of IEEE, IFAC, IET, SAEng; PEng)

Abstract: In the literature of control science and engineering, we are more concerned with convenrgence of the states of any dynamic systems, and gradually pay attention to academically challenging and practically relevant finite time convergence where finite-time control drives the states converge within a certain time moment, regardless of how each state element converges even though it is an very important and critical issue in many accurate, precise and delicated operations. In this report, we first introduce various fundamental and basic ideas of time-synchronized control, moving the boundary well beyond the well-established notions and outcomes of standard “finite-time stability”. Then, we introduce a control problem with unique finite/fixed-time stability considerations, namely time-synchronized control, where all the system state elements converge to the origin at the same time. Finally, we share with you more nice properties for this interesting time-synchronized property attained, e.g., shortening the travel length and reducing the energy consumption. We welcome interest excellent individuals further push the boundary further beyond!

Biography: Shuzhi Sam Ge, IEEE Fellow (S’90-M’92-SM’99-F’06), PhD, DIC, BSc, PEng, is a Professor with the Department of Electrical and Computer Engineering, The National University of Singapore, Singapore. He received the Ph.D. degree from the Imperial College London, London, U.K., in 1993, and the B.Sc. degree from the Beijing University of Aeronautics and Astronautics, China, in 1986. He serves as the Founding Editor-in-Chief, International Journal of Social Robotics, Springer Nature, 2008-present, a member of the standing committee of International Conference on Social Robotics, 2009-present, a member of the Steering Committee of Asian Control Association, 2020-Present, and Vice-Chair, Technical Committee on Computational Intelligence in Control, IFAC, 2014-Present, and book Editor for Automation and Control Engineering of Taylor & Francis/CRC Press. He has served/been serving as an Associate Editor for a number of flagship journals including IEEE TAC, IEEE TCST, IEEE TNN, IEEE Transaction on SMC-Systems, and Automatica, CAAI Transactions on Intelligence Technology. At IEEE Control Systems Society, he served as Vice President for Technical Activities, 2009-2010, Vice President of Membership Activities, 2011-2012, Member of Board of Governors of IEEE Control Systems Society, 2007-2009. He is Clarivate Analytics (former Thomson Reuters) high-cited scientist in 2016-2020, Elsevier high-cited scientist in 2014-2019. He is also a Fellow of IFAC, IET, and SAEng. His current research interests include robotics, intelligent systems, artificial intelligence, and smart materials.

Prof. Jay H. Lee
Department of Chemical & Biomolecular Engineering, KAIST, Korea
(General Chair, IFAC World Congress 2026)

Abstract: Since Alan Turing’s remarkable foresight of creating a machine that simulates the “adult brain” starting from the “child mind” through a computer algorithm that educates through rewards and punishments, reinforcement learning (RL) has been at the forefront of many academic fields including psychology, computer science, and control. With recent advancement of deep learning and GPU-computing as well as well-publicized success stories like the Alpha-Go, it is enjoying a renaissance of popularity and offers opportunities for applications with commercial impacts. RL and control originated from the different fields but they both address the same basic problem of making sequential decisions in an uncertain, dynamic environment to maximize/minimize a long-term objective function. In this presentations, similarities and differences between reinforcement learning and optimal control will be brought to attention and some ideas will be shared on how they can be brought to complement and support each other in solving complex industrial decision problems. Some exemplary applications expected to benefit significantly from the use of RL concepts and methods will be presented, including batch process control, energy planning, and materials design.

Biography: Jay H. Lee obtained his B.S. degree in Chemical Engineering from the University of Washington, Seattle, in 1986, and his Ph.D. degree in Chemical Engineering from California Institute of Technology, Pasadena, in 1991. From 1991 to 1998, he was with the Department of Chemical Engineering at Auburn University, AL, as an Assistant Professor and an Associate Professor. From 1998-2000, he was with School of Chemical Engineering at Purdue University, West Lafayette and then with the School of Chemical Engineering at Georgia Institute of Technology, Atlanta. In 2010, he joined Korea Advanced Institute of Science and Technology (KAIST) as the Head of the Chemical and Biomolecular Engineering Department. He recently served as the Associate Vice President of International Office and is currently serving the role of International Relations Advisor to the President. He is also the founding director of Saudi Aramco-KAIST CO2 Management Center. He has held visiting appointments at E. I. Du Pont de Numours, Wilmington, in 1994 and at Seoul National University, Seoul, Korea, in 1997.

He was a recipient of the National Science Foundation’s Young Investigator Award in 1993 and also the AIChE CAST Computing in Chemical Engineering Award in 2013. He was elected as an IEEE Fellow in 2011, an IFAC Fellow in 2011, and an AIChE Fellow in 2013. He is a member of both the Korean National Academy of Science and Technology (KAST) and the National Academy of Engineering Korea (NAEK). He was also the 29th Roger Sargent Lecturer in 2016. He published over 200 manuscripts in SCI journals with more than ~14000 Google Scholars citations (with h-index of 53). His research interests are in the areas of state estimation, robust control, model predictive control, planning/scheduling, and approximate dynamic programming with applications to energy systems and carbon management systems.

Prof. Huei Peng
Department of Mechanical Engineering, University of Michigan, USA
(Roger L. McCarthy Professor)

Abstract: Automatic Control is a relatively new field in Mechanical Engineering. However, given the pressing recent challenges of climate change, the pandemic, the emergence of the 3rd wave of AI, and fast develop of fields such as mobility and robotics, it is time to rethink the core element of automatic controls and how we ensure our students are best equipped with knowledge to address these new society challenges.
There are several external factors that seem to impact our field of discipline at this very moment. One is the rapid rising of “data-driven” and “computation-driven” tools and methods, including deep-neural-network and reinforcement learning. These methods are quite different from the traditional dynamic modeling, and subsequent “model-based” analysis and synthesis methodologies that dominate what we teach in undergraduate and graduate control courses.
Another mega-trend is that many “grand challenge” problems emerge and need to be solved quickly, including climate change, clean energy, etc. Due to the urgency and multi-disciplinary nature of these problems, the cry for “problem-centric” research vs. “domain-centric” research is stronger and government funding seems to be shifting very quickly. We need to think carefully how to best-preparing our students to be ready to take on these grand challenges. The evolution of my research areas and some preliminary experience and thoughts will be presented in this talk.

Biography: Huei Peng received his Ph.D. in Mechanical Engineering from the University of California, Berkeley in 1992. He is now a Professor at the Department of Mechanical Engineering at the University of Michigan. His research interests include adaptive control and optimal control, with emphasis on their applications to vehicular and transportation systems. His current research focuses include design and control of electrified vehicles, and connected/automated vehicles. In the last 10 years, he was involved in the design of several military and civilian concept vehicles, including FTTS, FMTV, Eaton/Fedex, and Super-HUMMWV—for both electric and hydraulic hybrid concepts. He served as the US Director of the DOE sponsored Clean Energy Research Center—Clean Vehicle Consortium, which supports more than 30 research projects related to the development of clean vehicles in the US and in China.

He currently serves as the Director of Mcity, which studies connected and autonomous vehicle technologies and promotes their deployment. He has served as the PI or co-PI of more than 50 research projects, with a total funding of more than 50 million dollars. He has more than 300 technical publications, including 140 in referred journals and transactions and four books. His h-index is 72 according to the Google scholar analysis. The total number of citations to his work is more than 20,000. He believes in setting high expectation and helping students to exceed it by selecting innovative research topics with real impact. One of his proudest achievements is that more than half of his Ph.D. students have each published at least one paper cited more than 100 times.
Huei Peng has been an active member of the Society of Automotive Engineers (SAE) and the American Society of Mechanical Engineers (ASME). He is both an SAE Fellow and an ASME Fellow. He is a ChangJiang Scholar at the Tsinghua University of China.