Prof. Jing Sun
(Keynote Speaker)

IEEE Fellow

University of Michigan, USA

Keynote Lecture: A Multi-scale Optimization Framework for Integrated Dynamic Systems

Abstract: Integrated systems are ubiquitous as more heterogeneous physical entities are combined to form functional platforms. With increased connectivity, new and “invisible” feedback loops and physical couplings are introduced, leading to emerging dynamics and making the integrated systems more control-intensive. The multi-physics, multi-time scale, and distributed-actuation natures of integrated systems present new challenges for modeling and control. Understanding their operating environments, achieving sustained high performance, and incorporating rich but incomplete data also motivate the development of novel design tools and frameworks.
      In this talk, I will use the integrated thermal and power management of connected and automated vehicles (CAVs) as an example to illustrate the challenges in the prediction, optimization, and control of integrated systems in the era of rapid advances in AI and data-driven control. While first-principle-based modeling is still essential in understanding and exploiting the underlying physics of the integrated systems, model-based control and optimization have to be used in a much richer context to deal with the emerging dynamics and inevitable uncertainties. For CAVs, we will show how model-based design, complemented by data-driven approaches, can lead to control and optimization solutions with a significant impact on energy efficiency and operational reliability, in addition to safety and accessibility.

Biography: Jing Sun received her Ph. D degree from the University of Southern California in 1989 and her master's and bachelor's degrees from the University of Science and Technology of China in 1984 and 1982, respectively. From 1989 to 1993, she was an assistant professor in the Electrical and Computer Engineering Department at Wayne State University. She joined Ford Research Laboratory in 1993, where she worked on advanced powertrain system controls. After spending almost ten years in the industry, she returned to academia in 2003. She joined the University of Michigan, where she is the Michael G. Parsons Collegiate Professor in the Naval Architecture and Marine Engineering Department, with joint appointments in the Electrical Engineering and Computer Science Department and Mechanical Engineering Department at the same university. She holds 44 U.S. patents and has published over 300 archived journal and conference papers. She is a Fellow of NAI (the National Academy of Inventors), IEEE (Institute of Electrical and Electronics Engineers), IFAC (International Federation of Automatic Control), and SNAME (the Society of Naval Architecture and Marine Engineering). She is a recipient of the 2003 IEEE Control System Technology Award.

Prof. Xinjun Liu
(Keynote Speaker)

Tsinghua University, China

Keynote Lecture: Transformative robotic machining technology and equipment for large-scale structures

Abstract: In this talk, the connotation of scientific and technological innovation and its role in the history of human development will be reviewed firstly. Then, the importance of scientific and technological innovation research and equipment innovation and its application in engineering will be emphasized. The scientific and technological innovation research models will be discussed and some examples about processing of complex structures and large structural parts will be given. This talk will focus on the conception, technical verification and engineering application of innovative research and development of industrial robots. Some expectations on the innovative development of robotic equipment will be shared at end of the talk.

Biography: Xinjun Liu is a Full Professor with Tenure in Department of Mechanical Engineering at Tsinghua University, Beijing, China. He is the "Cheung Kong" Chair Professor, and the winner of National Outstanding Youth Fund of China. He is currently the MO Chair of International Federation for the Promotion of Mechanism and Machine Science (IFToMM) China-Beijing and the Director of the Beijing Key Laboratory of Precision and Ultra-precision Manufacturing Equipment and Control. From 2000 to 2001, he worked as a Postdoctoral Researcher at Tsinghua University. He was a Visiting Researcher at Seoul National University, Seoul, Korea in 2002-2003. He was the Alexander von Humboldt (AvH) Research Fellow at University of Stuttgart in Germany from 2004 to 2005. He was the Visiting Professor with Prof. Dr. Reimund Neugebauer at Fraunhofer Institute for Machine Tools and Forming Technology, Germany, in August of 2007. He has published over 190 papers in refereed journals and refereed conference proceedings and has been selected as Elsevier's Most Cited Chinese Researchers for seven consecutive years from 2014 to 2020, 90 authorized patents, and three books (including one book in English). His research interests include smart robotics, parallel mechanisms and robots, machining robots, and advanced and smart manufacturing equipment.

Prof. Ji-Hong Li
(Keynote Speaker)

Korea Institute of Robotics and Technology Convergence, South Korea

Keynote Lecture: Navigation, Guidance, and Control of Marine Vehicles

Abstract: Following a brief overview of some of recent advancements in the navigation, guidance, and control of marine vehicles at KIRO over the past decade, this talk will primarily address the control issue, especially the trajectory tracking problem for a class of underactuated marine vehicles.
     Trajectory tracking of underactuated system has been a challenging and also interesting topic in the nonlinear control community over the past decades. Based on the different ways of reference trajectory design, this trajectory tracking problem usually can be classified into two categories: trajectory tracking and path following, where the former one further can be divided into the traditional trajectory tracking and trajectory following. This talk addresses the traditional trajectory tracking problem for a class of underactuated marine vehicles. Compared to the previous related works where the proposed methods usually depended on the vehicle’s specifically simplified dynamics and also needed various restricting conditions on the reference trajectory, in this talk we try to provide a sort of general form of controller with general form of vehicle’s dynamics and also without any restriction on both of the vehicle’s dynamics and the reference trajectory.

Biography: Ji-Hong Li is a chief researcher in Korea Institute of Robotics and Technology Convergence, Republic of Korea. He received his B.S. in physics from Jilin University, China, in 1991; M.E. in 1999 and Ph.D. in 2003 both in Electronics Engineering from Chungnam National University, Korea. Dr. Li currently is an adjunct professor in Pukyong National University, Korea, and also a guest professor in Shenyang Institute of Automation, Chinese Academy of Sciences, China. He has published more than 100 peer-reviewed papers and dozens of patents. His current research interests mainly focus on the navigation, guidance, and control of various marine robotics. He is the winner of 26th Korean “Ocean Day” Minister Award, Ministry of Oceans & Fisheries, Korea, and the winner of 2021 Korean Top 100 Outstanding R&D Achievements, and as well as several best paper awards in the marine robotics related academic conferences. In addition, he is the board member of Korea Marine Robot Technology Society and the chair of Marine Unmanned Systems Society, as well as IEEE senior member, and IFAC TC2.3, TC7.2 members.

Prof. Dong Eui Chang
(Keynote Speaker)

Korea Advanced Institute of Science & Technology, South Korea

Keynote Lecture:Feedback integrators: A new method for structure-preserving numerical integration of dynamical systems

Abstract: Structure-preserving numerical integration for ordinary differential equations is very crucial in numerical simulation of dynamical systems. In general, numerical integration of ordinary differential equations is expected to preserve first integrals and state-space manifolds such as energy, angular momentum and SO(3) for the free rigid body dynamics. As such, structure-preserving integration has been a vast research area for which various algorithms have been developed such as symplectic integrators, variational integrators and so on. Most of the algorithms, however, require special tricks, case-by-case, such as solving implicitly defined algebraic equations at each integration step or using a particular parameterization of a given manifold.
     In this talk, I will present a new method of structure-preserving integration, called feedback integrators, which does not require any of these special tricks but rather allows one to generally use any off-the-shelf numerical integrators such as the Euler method and the Runge-Kutta method, in order to numerically integrate a given dynamical system while preserving its conserved quantities. Feedback integrators apply to holonomic mechanical systems and non-holonomic mechanical systems as well as regular mechanical systems. They also extend to controller design for systems defined on manifolds. The theory of feedback integrators is still in the making to which everyone is welcome.

Biography: Dong Eui Chang is a professor in the School of Electrical Engineering at KAIST, Head of the SeongNam-KAIST Next-Generation ICT Research Center, and Head of the KAIST-HwaSeong Science Hub. He served in 2021-2022 on the advisory board for Presidential Security Service for President of South Korea. He received his B.S. degree in Control & Instrumentation Engineering in 1994, his M.S. degree in Electrical Engineering in 1997, both from Seoul National University, and his Ph.D. degree in Control & Dynamical Systems from Caltech in 2002 under the supervision of Professor Jerrold E. Marsden. He was an associate professor in the Department of Applied Mathematics at the University of Waterloo before he joined KAIST in 2017. He is a co-author of the book “Deep Neural Networks in a Mathematical Framework” published by Springer in 2018. His research interests include control, robotics, mechanics, and AI.

Prof. Chunhui Zhao
(Invited Speaker)

Zhejiang University, China

Invited Lecture: Theoretical exploration and practice of industrial process fault diagnosis based on zero sample learning

Abstract: Fault diagnosis system is an important guarantee for the safe and reliable operation of industrial processes. Data-driven fault diagnosis modeling often depends on the collected sufficient historical fault data. However, in actual industrial processes, it is common that process failures have no historical samples and no labels. In this regard, we need to deal with a very challenging fault diagnosis task, that is, to consider diagnosing when there are no historical fault samples available for model training. We introduced the concept of zero-shot learning into the industrial field for the first time, and innovatively established a zero-shot-learning fault diagnosis method. By skillfully introducing a priori modeling knowledge with fault description as the carrier and adopting the attribute migration method, we overcame the concerned bottleneck problem that traditional fault diagnosis research cannot meet the sample size constraint. We theoretically analyzed and explained the effectiveness and feasibility of the zero-shot-learning diagnosis method based on fault description. In addition, the fault diagnosis performance in the case of zero samples is verified in the real megawatt thermal power process, and the results show that it is feasible to diagnose unseen target fault without samples. On this basis, the existing challenges, difficulties and future research directions are revealed.

Biography: Chunhui Zhao, Qiushi Distinguished Professor,and academic vice president of East China Jiaotong University. She is the recipient of the National Outstanding Youth Fund, and recipient of the Chinese Young Female Scientist Award, Fellow of Chinese Association of Automation.
     Her research interests include statistical machine learning and data mining for industrial application. She has authored or coauthored more than 220 papers in peer-reviewed international journals. She has published 3 monographs and one national textbook. She authorized more than 60 invention patents. She has hosted more than 20 scientific research projects, including National Natural Science Foundation of China project, National key R&D project, provincial projects and corporate cooperation projects. She has received the Ministry of Education Natural Science Award and other provincial and ministerial awards. She also received more than ten academic awards, including the First Prize of Natural Science of Chinese Association of Automation, the First Young Women Scientists Award of Chinese Association of Automation, etc. She has served AE of three International Journals, including Journal of Process Control, Control Engineering Practice and Neurocomputing, and three domestic journals, including Control and Decision, etc.

Assoc. Prof. Yue Gao
(Invited Speaker)

Shanghai Jiaotong University, China

Invited Lecture: Advancements in Learning-Based Control: Transforming the Mobility of Legged Robots and Their Real-World Applications

Abstract: Legged robots have a broad spectrum of applications, primarily due to their adaptation when traversing unstructured environments. Their complexity arises from diverse configurations such as bipedal, quadrupedal and hexapedal, extending to control models as multi-input, multi-output, multi-end-effector systems. This complexity introduces significant challenges in locomotion control and planning, including model identification, real-time computational demands, and adaptation to unseen tasks. The purpose of this talk is to provide an overview of our new research in learning-based control for legged robots. Leveraging advancements in reinforcement learning and deep learning, we have significantly enhanced legged robots' mobility, safety, and the ability of self-learning. Furthermore, this talk will highlight an interesting application of our research through the curling and skiing six-legged robots designed for Beijing Winter Olympics, demonstrating the real-world application potentials for learning-based control for legged robots.

Biography: Yue Gao, an associate professor and doctoral advisor at the School of Electronic Information and Electrical Engineering, AI Institute, Shanghai Jiao Tong University. She obtained her Bachelor of Science in Computer Science from the University of Wisconsin-Madison in 2008, she received Master of Science in Computer Science from Cornell University in 2012 and Ph.D.in Computer Science from Cornell University in 2016. Her research focuses on the control and planning for legged robots. Over the past five years, she has published more than 40 papers in SCI/EI journals, served as Principal Investigator for projects from National Natural Science Foundation and National Key Research and Development Program in China. Currently, she serves as an Associate Editor for Robotica journal. She has received awards as the Outstanding AI Leader at the World Artificial Intelligence Conference and the first prize in the National University Teaching Innovation Competition.

Dr. Yuyang Zhou
(Invited Speaker)

Edinburgh Napier University, UK

Invited Lecture: Fully Probabilistic Control Algorithm Design for a class of Complex Stochastic Systems

Abstract: Complex dynamical systems have garnered significant interest in the realms of control and engineering, as they offer a cohesive and natural framework for the mathematical modeling of diverse real-world systems, such as communication networks, power grids, and chemical processes. The inherent characteristics of these systems, such as high dimensionality, intricate structures, complex models with multiple modes of switching, and substantial uncertainties, pose notable challenges for system analysis, estimation, and particularly for control.
     This talk will introduce a novel decentralised probabilistic control framework designed for complex stochastic systems. It will offer a fresh research outlook dedicated to refining the control of such systems. In this talk, we will delineate the core principles and applications of the framework, showcasing its contribution to the development of innovative control strategies for complex stochastic systems.

Biography: Yuyang Zhou received her B.Sc. in Electrical & Electricity Engineering School in The University of Manchester, United Kingdom, 2014 and Electrical & Engineering School in Beijing JiaoTong University, China, 2014, and later her Ph.D. in control theory from Electrical & Electricity Engineering School in The University of Manchester, United Kingdom, 2018. After that, she became a Research Associate for three years in the Faculty of Engineering and Applied Science at Aston University, Birmingham, United Kingdom. She currently is a lecturer in group of Engineering and Mathematics, School of Computing, Engineering and Built Environment, Edinburgh Napier University, United Kingdom. She is also a guest editor for MDPI mathematics. Her current research focuses on probabilistic and stochastic control, minimum entropy control, pdf control and decentralized control, time-delay systems, networked and complex systems control with applications to power grids and Kalman filtering.