Keynote Speaker
Biography: Prof. Mingcong Deng (IEEE Fellow) received his PhD in Systems Science from Kumamoto University, Japan, in 1997. From 1997.04 to 2010.09, he was with Kumamoto University; University of Exeter, UK; NTT Communication Science Laboratories; Okayama University. From 2010.10, he has been with Tokyo University of Agriculture and Technology, Japan, as a professor. Now he is the Chair of Department of Electrical and Electronic Engineering. Prof. Deng specializes in three complementary areas: Operator based nonlinear fault detection and fault tolerant control design; System design on human factor based robot control; Learning based nonlinear control. Prof. Deng has over 650 publications including 220 journal papers in peer reviewed journals including IEEE Press and other top tier outlets. He serves as a chief editor for 2 international journals, and associate editors of 6 international journals. Prof. Deng is a co-chair of agricultural robotics and automation technical committee, IEEE Robotics and Automation Society; Also a chair of the environmental sensing, networking, and decision making technical committee, IEEE SMC Society. He was the recipient of 2014 & 2019 Meritorious Services Award of IEEE SMC Society, 2020 IEEE RAS Most Active Technical Committee Award (IEEE RAS Society) and 2024 IEEE Most Active SMC Technical Committee Award (IEEE SMC Society). He is a fellow of The Engineering Academy of Japan.
Speech Title: Learning & Operator Theory Based Nonlinear Control of Micro Hands
Abstract: Learning & operator theory based robust nonlinear control design for nonlinear systems with uncertainties is shown. The relationship between operator theory and learning algorithms is discussed. Meanwhile, I will introduce support vector regression (SVR) with generalized Gaussian distribution (GGD) kernel utilized for nonlinear model matching. Further, learning and operator based robust nonlinear internal model control design is shown. Finally, some current results using the above mentioned schemes are introduced to actuator position control of 2D/3D micro hands.
Biography: Abderazek Ben Abdallah is Regent, Dean, and Full Professor in the School of Computer Science and Engineering at the University of Aizu, Japan. He has served on the university’s Education and Research Council since 2014 and previously led its Computer Engineering Division from 2014 to 2022. He received his Ph.D. in Computer Engineering from the University of Electro-Communications (UEC), Tokyo, in 2002.
With more than two decades of academic leadership, Dr. Ben Abdallah’s research focuses on high-performance and energy-efficient computing systems, spanning computer architecture, neuromorphic circuits and systems, fault-tolerant on-chip networks, and embedded systems. He is the author of four books, including Neuromorphic Computing: Principles and Organization-recognized by BookAuthority in June 2025 as the best neuromorphic computing book of all time-and Multicore Systems-on-Chip: Practical Software/Hardware Design, which has been translated into Chinese. His scholarly record includes over 160 peer‑reviewed publications, 17 competitive research grants, and 14 patents (9 registered, 5 provisional). His recent innovations include leading the development of the world’s first AI-powered off-grid solar carport for renewable energy storage (2022–2023) and directing a full-cycle parallel processor design project (2002–2007) covering ISA design, hardware prototyping, and cycle-accurate simulation.
Speech Title: Neuromorphic Humanoid Robotics: Event-Driven Intelligence and Distributed Autonomy
Abstract: Neuromorphic engineering is redefining the future of humanoid robotics by enabling ultra-low-power intelligence, real-time responsiveness, and adaptive behavior beyond the limits of conventional AI architectures. This keynote explores a full-stack neuromorphic approach to embodied intelligence, integrating event-driven sensing, spiking neural control, and continual on-chip learning to support robust perception, decision-making, and motor coordination in dynamic environments. Drawing on advances in neuromorphic circuits, fault-tolerant on-chip networks, and energy-efficient processor design, the talk highlights how brain-inspired computation can enhance locomotion, manipulation, and human–robot interaction.
Building on this foundation, the keynote introduces a complementary analytical framework for task allocation in distributed anthropomorphic robots operating in mission-critical environments. The model captures the interplay between autonomy, resource constraints, and task urgency, enabling decentralized coordination under intermittent connectivity, limited energy, and unpredictable workloads. By combining neuromorphic intelligence at the individual robot level with principled coordination strategies at the system level, this work outlines a path toward scalable, resilient humanoid robot teams capable of operating effectively in high-stakes, real-world scenarios such as disaster response, emergency healthcare, and autonomous field operations.
Biography: HeSuan Hu (Senior Member, IEEE) received the BS degree in computer engineering and the MS and PhD degrees in electro-mechanical engineering from Xidian University, Xi’an, China, in 2003, 2005, and 2010, respectively. He is currently a full professor with Xidian University. He is a holder of more than 40 issued and filed patents in his fields of expertise. His current research interests include discrete event systems and their supervisory control techniques, Petri nets, automated manufacturing systems, multimedia streaming systems, autonomous vehicles, cyber security, and artificial intelligence. He has more than 190 publications in journals, book chapters, and conference proceedings in the above areas. He was a recipient of many national and international awards, including the Franklin V. Taylor Memorial Award for Outstanding Papers from the IEEE SMC Society, in 2010 and the finalists of the Best Automation Paper from the IEEE ICRA Society, in 2013, 2016, and 2017. He has been an associate editor of the IEEE Control Systems Magazine, IEEE Robotics and Automation Magazine, IEEE Transactions on Automation Science and Engineering, IEEE Robotics and Automation Letters, and Journal of Intelligent Manufacturing. He is an IEEE distinguished lecturer.
Biography: Dr. Lu Liu received her Ph.D. degree from the Chinese University of Hong Kong. From 2009 to 2012, she was an Assistant Professor at The University of Tokyo, Japan, and then a Lecturer at The University of Nottingham, United Kingdom. Then she joined the City University of Hong Kong and is currently a Full Professor. Her research interests are primarily in networked dynamical systems, unmanned systems, robotics, and intelligent control. She is a Clarivate Highly Cited Researcher. She received several best paper awards in flagship conferences, including the Guan Zhaozhi Award of the 27th Chinese Control Conference in 2008, and the Shimemura Young Author Award of the 11th Asian Control Conference in 2017. She received the Excellent Young Scientists Fund (Hong Kong and Macao) from the National Nature Science Foundation of China (NSFC) in 2022. Dr. Liu has served as an Associate Editor of IEEE Transactions on Cybernetics, IEEE Transactions on Fuzzy Systems, IEEE Robotics and Automation Letters, Control Theory and Technology, and Unmanned Systems. She served in the organizing committee of several international conferences including General Chair of the 2022 IEEE International Conference on Real-Time Computing and Robotics, General Chair of the 2022 IEEE International Conference on Control and Automation.
Speech Title: Distributed Cooperative Control under Resource-Constrained Network
Abstract: Cooperative control of multi-agent systems has attracted significant interest in the systems and control community due to its potential for impactful real-world applications, such as search and rescue by a team of unmanned ground/aerial vehicles, and ocean sampling using a fleet of underwater gliders. In this talk, we will present the cooperative output consensus problem for heterogeneous linear multi-agent systems under resource-constrained networks. We will begin with an overview of the event-triggered control paradigm, highlighting its efficiency and practicality by reducing unnecessary communication and control updates. Then we will present a novel distributed event-triggered control strategy tailored for the cooperative output consensus of multi-agent systems under switching network topologies. This strategy ensures that output consensus is achieved asymptotically while reducing communication demands, thus conserving valuable system resources. Notably, the continuous monitoring issue can be avoided.