Prof. Yongduan Song
(Keynote Speaker)

IEEE Fellow
Chongqing University, China

Keynote Lecture: TBA

Abstract: TBA

Biography: David Yongduan Song, Fellow of IEEE, Fellow of International Eurasian Academy of Sciences, Fellow of Chinese Automation Association.

He received his Ph.D. degree in electrical and computer engineering from Tennessee Technological University, Cookeville, TN, USA, in 1992. He held a tenured full professor position with North Carolina Agricultural and Technical State University, Greensboro, NC, USA, from 1996 to 2008, and a Langley Distinguished Professor position with the National Institute of Aerospace, Hampton, VA, USA, from 2005 to 2008. He was one of the six Langley Distinguished Professors with the National Institute of Aerospace (NIA), and the Founding Director of the Center for Cooperative Systems with NIA. He is currently the Dean of the School of Automation, Chongqing University, Chongqing, China, and the Founding Director of the Institute of Smart Engineering, Chongqing University.

Dr. Song is a leading researcher in neural networks (NN) based adaptive control, significantly contributing to both NN theory methods and engineering applications. He is very active as associate editors for top IEEE journals, including IEEE Trans. on Neural Networks, IEEE Trans. on Automatic Control, IEEE Trans. Systems, Man, and Cybernetics, IEEE Trans. on Intelligent Transportation Systems, IEEE Trans. on Cognitive and Developmental Systems. As a scientific leader in the field of systems and control, he has been serving on various national and international technical committees.

Prof. Song has made original contributions in neural network adaptive control of nonlinear systems with real world applications, which can be assessed by his publications (over 200 papers) in prestigious international journals, including IEEE T-NNLS, IEEE T-FS, IEEE T-SMC, IEEE T-Cybernetics, IEEE T-AC, IEEE T-IE and Automatica. He authored/co-authored 11 books in the field of control and artificial intelligence. He also held over 50 patents, and has given numerous keynote speeches and invited talks, chaired several conferences.

Prof. Mitsuhiro Hayashibe
(Keynote Speaker)

Tohoku University, Japan

Keynote Lecture: Motor Synergy Emergence in Redundancy and Synergistic Whole-body Motion Generation through Deep Reinforcement Learning

Abstract: In recent studies, Deep Reinforcement Learning (DRL) has shown promising results for quadruped robots. However, training humanoids with full degrees of freedom remains a significant challenge for the very high computational cost. The high-dimensional action space complicates the optimization process for generating expected movement patterns, particularly for different stylized motions and for different motion frequencies. Therefore, we propose an AI-CPG algorithm, which integrates central pattern generators with DRL and facilitates the learning of complex movement patterns without intricate reward function designs. The point of this proposed study is to connect reinforcement learning and imitation learning through the bio-inspired structure of CPG (Central Pattern Generators).

In addition, neuroscientific study on how motor synergy emerge in redundancy during learning process is also introduced to understand why humans use low-dimensional synergy structure for energy efficiency. Humans exploit motor synergies for motor control; however, how they emerge during motor learning is not clearly understood. Few studies have dealt with the computational mechanism for generating synergies in whole-body dimensionality. Interestingly, increasing the weight of symmetry loss resulted in increased energy efficiency and synergetic motion patterns concurrently. This suggests that locomotor synergies can emerge through learning processes, complementing the understanding of synergy emergence mechanisms.

Biography: Dr. Mitsuhiro Hayashibe is a Professor at the Department of Robotics, Graduate School of Engineering, Tohoku University, Japan, and founder of the Neuro-Robotics Lab since 2017. He is concurrently with the Graduate School of Biomedical Engineering, Tohoku University. He obtained PhD at University of Tokyo in 2005, and the Habilitation degree at University of Montpellier in 2015. He was previously an Assistant Professor with the Department of Medicine, Tokyo Jikei University School of Medicine (東京慈恵会医科大学医学部) for 2001-2006, and a Tenured Research Scientist with INRIA (Institut National de Recherche en Informatique et en Automatique, フランス国立情報学自動制御研究所) and University of Montpellier, France for 2008-2017. He has been a visiting researcher at RIKEN Center for Brain Science and TOYOTA Collaboration Center (理化学研究所 脳神経科学研究センター) since 2012, also at EPFL (École Polytechnique Fédérale de Lausanne, スイス連邦工科大学ローザンヌ校) for 2016 with Swiss National Science Foundation grant. He is co-chair of IEEE Robotics and Automation Society Technical Committee on Human Movement Understanding. He was awarded with the 15th Annual Delsys Prize 2017 for Innovation in Electromyography from De Luca Foundation, USA.

Prof. Hongliang REN
(Keynote Speaker)

The Chinese University of Hong Kong (CUHK), Hong Kong, China

Keynote Lecture: Compliant surgical motion generation and perception towards intelligent minimally invasive robotic procedures

Abstract: Minimally Invasive Surgeries (MIS) emerging in modern medical treatment have brought new opportunities and challenges for procedure-specific surgical motion generation and the associated motion understanding, which are the foundation of intelligent robotic manipulation and guiding interventions. Image-guided robotic surgery is expected to increase the precision, flexibility, and repeatability of surgical procedures but poses challenges for system development. This talk will highlight our recent developments in dexterous robotic motion generation with motion perception towards intelligent image-guided minimally invasive procedures. The procedure-specific telerobotic surgical systems can assist surgeons in performing dexterous manipulations using continuum motion generation mechanisms with variable stiffness and context awareness.

Biography: Prof. Hongliang Ren received his Ph.D. in Electronic Engineering (Specialized in Biomedical Engineering) from The Chinese University of Hong Kong (CUHK) in 2008. He has served as an Associate Editor for IEEE Transactions on Automation Science & Engineering (T-ASE) and Medical & Biological Engineering & Computing (MBEC). He has navigated his academic journey through Chinese University of Hong Kong, Johns Hopkins University, Children’s Hospital Boston, Harvard Medical School, Children’s National Medical Center, United States, and National University of Singapore (NUS).

His areas of interest include biorobotics, intelligent control, medical mechatronics, soft continuum robots, soft sensors, and multisensory learning in medical robotics. He is the recipient of CUHK Young Researcher Award, NUS Young Investigator Award and Engineering Young Researcher Award, IAMBE Early Career Award 2018, Interstellar Early Career Investigator Award 2018, ICBHI Young Investigator Award 2019, and Health Longevity Catalyst Award 2022 by NAM & RGC, Best Paper Awards in IEEE-ROBIO (2019 & 2013), IEEE-RCAR2016, IEEE-CCECE2015, IEEE-Cyber2014 among 30+ others awards.

He has been constantly listed among the world’s top 2% of the most-cited scientists by Stanford University in the career-long category.

He frequently served as an expert reviewer/judge for international funding agencies (60+ proposal reviews) of 10+ countries/regions (including Switzerland, Belgium, UK, Kazakhstan, Poland, Hong Kong, Macau, Chilean, China, Singapore etc.)

Prof. Huichao DENG
(Keynote Speaker)

Beihang University, China

Keynote Lecture: From Insects to Innovation: Biomimetic Flapping-Wing Micro Air Vehicles for Agile and Efficient Flight

Abstract: The remarkable agility and precise hovering capabilities of insects and birds have long fascinated researchers. Over the past few decades, biologists have made significant progress in uncovering the unsteady aerodynamic forces generated by flapping wings. Meanwhile, advancements in MEMS technology have paved the way for the development of flapping-wing micro air vehicles (FW-MAVs) that mimic the flight mechanics of these natural flyers. While fixed-wing and rotary-wing designs currently dominate the drone market, flapping-wing propulsion is emerging as a promising alternative for confined environments. FW-MAVs offer several advantages, including enhanced maneuverability, high aerodynamic efficiency at relatively low speeds, and improved safety—thanks to their lightweight structures and flexible wings which can absorb impacts during in-flight collisions. This talk will present recent advancements in FW-MAV technology inspired by nature. We will discuss key developments in hover-capable FW-MAVs, covering aspects such as mechanism design, aerodynamic optimization, vehicle dynamics, and attitude control. Finally, we will explore future directions and challenges in this rapidly evolving field.

Biography: Professor Huichao Deng received her B.Eng. degree in electrical engineering, M.Eng. degree in control science, and Ph.D. degree in electrical engineering from the Harbin Institute of Technology, Harbin, China, in 2008, 2010, and 2015, respectively. From 2010 to 2011, she was a visiting scholar at the Massachusetts Institute of Technology (MIT), conducting research on advanced control strategies in robotics. From 2019 to 2020, she was a visiting researcher at the Department of Mechanical Engineering of Stanford University, focusing on the design and optimization of bio-inspired aerial systems. Currently, she is a Professor at the Robotics Institute of Beihang University. Her research focuses on multi-functional flying robots, biomimetic design, and micro-energy systems.

Prof. David Banjerdpongchai
(Keynote Speaker)

Chulalongkorn University, Thailand

Keynote Lecture: Design of Supervisory Model Predictive Control for HVAC Systems

Abstract: This talk presents recent trends in energy intensity and energy demand in ASEAN and addresses key energy policies as part of Thailand 4.0. Electricity consumption has been increasing significantly due to the earth’s rising global temperatures, leading to higher energy usage in Heat, Ventilation and Air Condition (HVAC) systems. Choosing the conventional setpoint temperature could reduce unnecessary power consumption, thus leading to cost savings. This talk presents the design of Supervisory Model Predictive Control (SMPC) for HVAC systems with multiple zones. The design aims to shave the peak demand and maintain occupants’ thermal comfort. Two methods of SMPC are developed, namely, centralized SMPC and decentralized SMPC. We apply the sparse Quadratic Programming (QP) solver using the interior point method. The results indicate that centralized supervisory control yields better outcomes, as demonstrated by a trade-off curve between total operating costs and thermal comfort. Moreover, centralized model predictive control successfully achieved satisfactory results in both tracking the reference signal and optimizing power consumption. Utilizing the sparse QP solver can yield faster computation compared to the standard QP solver, making it more suitable for the design of SMPC.

Biography: David Banjerdpongchai has been with the department of Electrical Engineering, Faculty of Engineering, Chulalongkorn University. Currently, he is a professor of Electrical Engineering, head of Center of Excellence in Intelligent Control Automation of Process Systems, and deputy director of International School of Engineering. He is a senior member of IEEE, a founding chair of IEEE Control Systems Society Thailand Chapter, Vice President of ECTI Association (2022-2023), President of ECTI Association (2024-2025), an executive board member of ECTI Association, and a chair of IEC TC65 Thailand National Committee. In the past, he served as a chair of Systems and Control Technical Committee of ECTI Association and a member of Steering Committee of Asian Control Association. He served as General Co-chair of ECTI-CON 2013, ICA-SYMP 2019, ECTI-CON 2024, ECTI-CON 2025, SICE Fes 2025, and TPC chair of ECTI-CON 2014 and TPC Co-chair of SICE 2020. He has served as an Associate Editor for IJCAS and a Section Editor-in-Chief for ASEAN Engineering Journal. He has published over 200 articles in journal and conference proceedings and a textbook on Dynamical Control Systems. His research interests are energy management systems, advanced process control, iterative learning control, and robust control applications.

Prof. Siti Anom Ahmad
(Keynote Speaker)

Universiti Putra Malaysia, Malaysia

Keynote Lecture: TBA

Abstract: TBA

Biography: Siti Anom Ahmad is a Professor at the Faculty of Engineering, Universiti Putra Malaysia. Siti Anom received her PhD in Electronics in 2009 and MSc in Microelectronics System Design in 2004 from the University of Southampton, UK. She received her BEng in Electronic/ Computer from UPM in 1999. She is a Professional Engineer of Board of Engineers Malaysia, Chartered Engineer of Institute of Engineering and Technology (IET), Senior Member of Institute of Electrical & Electronic Engineering (IEEE) and a member of the Institute of Engineers Malaysia (IEM). Her research interests are biomedical engineering, artificial intelligence, gerontechnology, and intelligent control systems.