Prof. Guangren Duan

Harbin Institute of Technology

Guang-Ren Duan is Fellow of CAA, IEEE, IFAC and IET, and Academician of the Chinese Academy of Sciences. He received his Ph.D. in Control Science and Engineering from Harbin Institute of Technology (HIT), Harbin, P. R. China, in 1989. After a two-year post-doctoral experience at the same university, he became professor of control systems theory at HIT in 1991. He visited the University of Hull, the University of Sheffield, and also the Queen's University of Belfast, UK, from December 1996 to October 2002. He is the founder and presently the Director of the Center for Control Theory and Guidance Technology at HIT. In 2021, he also established the automation faculty at the Southern University of Science and Technology (SUSTech), Shenzhen, China, and is presently serving as the dean for the School of Automation and Intelligent Manufacturing at SUSTech. 

He is the author and co-author of five books and over 600 SCI-indexed publications. His research interests include both linear and nonlinear control, and their applications in spacecraft, robotics. He founded the TC on Fully Actuated System Approach, IEEE SMC Society, and has been general chairs for several international conferences including the 23rd IFAC Symposium on Automatic Control in Aerospace, and has been invited to give plenary talks at more than 40 international conferences, including IFAC TDS 2021, IEEE ARM 2020, IEEE ICRA 2021, IEEE IECON 2023, SICE-ICASE 2006, SICE 2014, CCC 2021, and CAC 2024. He is ranked No.1 in the subfield of Industry Engineering and Automation in the Elsvier-Stanford “World’s Top 2% Scientists” program by Single Recent Year Scientific Impact (2025) .

Title

FAS Approach: From State Feedback to Output Feedback

Abstract

It is well-known that physical fully actuated systems (FASs) are a perfect type of systems in the sense that their controllers can be easily designed and the resulted closed-loop systems can often be made globally constant linear. A non-FAS is a system that is either a physical under-actuated system (UAS) or a system that cannot be classified into a FAS or UAS. If, by any chance, a non-FAS can be converted into a FAS, then the control design of all non-FASs can be systematically solved. However, under the traditional definitions and physical restrictions, this goal is not practical and in general not achievable. Fortunately, a recently significant achievement on the discovery of the mathematically generalized FAS model of dynamical systems made an important milestone toward this goal. Although a non-FAS cannot be converted into a physical FAS, it can often be converted into a mathematically generalized FAS. Moreover, like a physical FAS, the control of a mathematically generalized FAS can also be easily realized. Such facts and logic naturally motivate the so-called FAS approach that solves control systems design based on generalized FAS models. 

In this talk, the backgrounds and the development of the FAS approach are briefly outlined, with an emphasis laid on output feedback control of nonlinear dynamical systems.

Prof. Yingwei Zhang

Northeastern University

Biography

Yingwei zhang, Professor and Doctoral Supervisor at Northeastern University. Obtained double bachelor's degrees from Harbin Institute of Technology and master's and doctoral degrees in Control Theory and Control Engineering from Northeastern University. Recipient of the National Outstanding Youth Science Fund, Changjiang Scholar Distinguished Professor of the Ministry of Education, recipient of the State Council Government Special Allowance, national level candidate for the Hundred, Thousand, and Ten Thousand Talents Project, Chief of the Science and Technology Innovation 2030- "New Generation Artificial Intelligence" Major Project, member of the Provincial Science and Technology Innovation Team, and member of the Provincial Political Consultative Conference. My research focuses on industrial intelligence technologies such as complex working condition recognition, autonomous digital twin, physical artificial intelligence, industrial big data and image science, process monitoring and quality prediction, machine learning and deep learning, multi-agent and evolutionary game theory, intelligent planning and resource scheduling, as well as engineering applications in steel manufacturing, large aircraft manufacturing, equipment chip manufacturing, energy industry, and rocket military industry.

Title

Intelligent sensing, intelligent control, intelligent scheduling, and AI security

Abstract

The new generation of artificial intelligence" achieves virtual-real twin evolution and inverse reasoning in manufacturing operations based on the industrial internet, innovating intelligent perception approaches for comprehensive integration of full-factor data in complex manufacturing systems. It provides methods to address intelligent control and scheduling challenges in open industrial internet environments, breaks through large-scale intelligent technology bottlenecks for cost reduction and efficiency improvement, and tackles group intelligent optimization decision-making technologies. From both theoretical and practical perspectives, it facilitates exchanges and discussions on the research status, progress, achievements (theoretical and experimental), and existing challenging issues of industrial internet and "new generation artificial intelligence" related technologies in intelligent perception, intelligent control, intelligent scheduling, and AI security.

Prof. Hairong Dong

Tongji University

Biography

Hairong Dong (Fellow, IEEE) received the Ph.D. degree from Peking University, Beijing, China, in 2002. She was a Visiting Scholar with the University of Southampton, Southampton, U.K., in 2006, and the University of Hong Kong, China, in 2008. She was also a Visiting Professor with the KTH Royal Institute of Technology, Stockholm, Sweden, in 2011. She is currently the Dean and a Professor at the School of Electronic and Information Engineering, Tongji University, Shanghai, China. Her current research interests include modeling and optimization of intelligent transportation systems, autonomous perception, collaborative control, artificial intelligence, etc. Prof. Dong is currently the Fellow of the Chinese Association of Automation.  She serves as Associate Editor for IEEE Trans-II, IEEE Trans-ICPS, IEEE OJIES, IEEE Trans-ITS, IEEE Trans-IV, IEEE Trans-CASII, IEEE ITS Magazine, etc.

Title

Key Technologies for Autonomous Rail Transit and AI-Enabled Maintenance

Abstract

As rail transit systems evolve toward networked, intelligent, and autonomous operation, increasingly complex environments, high-speed conditions, diverse safety risks, and growing maintenance demands pose significant challenges to perception, decision-making, control, and maintenance. 

This talk presents recent advances in key technologies for autonomous rail transit and intelligent maintenance, focusing on holistic perception, embodied autonomous driving, cooperative control, intelligent scheduling, and maintenance intelligence. Foundation models, embodied intelligence, and digital twins are explored for multi-source information fusion, proactive environment understanding, autonomous decision-making, cooperative optimization, and the development of an AI-enabled maintenance foundation framework. By establishing a closed-loop architecture integrating perception, decision-making, control, and maintenance, these technologies support the development of safe, resilient, efficient, and intelligent next-generation rail transit systems.

Prof. Yungang Liu

Shandong University

Biography

Prof. Yungang Liu is the Director of the Key Laboratory of Machine Intelligence and System Control, Ministry of Education of China; the Director of the Institute of Artificial Intelligence and Systems and Control, SDU; the Director of the Technical Committee on Artificial Intelligence and Machine Vision, Shandong Institute of Electronics; and the Director of the SDU-IBM Research Center on Big Data and Analytics. He is also the Vice Director of the Engineering Research Center of Intelligent Unmanned System, Ministry of Education of China. His current research interests include stochastic control, nonlinear control design and system analysis, cooperative control, distributed parameter systems, adaptive control and applications, robots and motion control, and artificial intelligence. 

Title

Hyperexponential stability and stabilization for uncertain nonlinear systems

Abstract

This talk discusses hyperexponential stability and hyperexponential stabilization for uncertain nonlinear systems. A definition of hyperexponential stability is first given in the widely-recognized manner. Its connections with the existing definitions of hyperexponential stability are identified. Necessary and sufficient conditions are then established to demonstrate the possibilities and impossibilities of pursuing hyperexponential stability. A time-varying-gain-based strategy is finally developed to achieve global hyperexponential stabilization for a class of uncertain nonlinear systems.

Prof. Yang Cong

South China University of Technology

Biography

Yang Cong is a full professor of Chinese Academy of Sciences. He received the B.S. degree from Northeast University in 2004, and the Ph.D. degree from State Key Laboratory of Robotics, Chinese Academy of Sciences in 2009. He was a Research Fellow of National University of Singapore (NUS) and Nanyang Technological University (NTU) from 2009 to 2011, respectively; and a visiting scholar of University of Rochester. His current research interests include robot vision, robot learning, big data, multimedia and medical image analysis. He won the National Science Fund (NSFC) for both Distinguished Young Scholars and Excellent Young Scholars, the first prize of Natural Science Award of Liaoning Province, the first prize of Natural Science Award of Chinese Association of Automation.  He has published more than 80 papers in the international journals and conferences. He served as the associated editor of IEEE Transactions on Circuits and Systems for Video Technology (TCSVT), Journal of Automation, and other well-known journals.

 Title

Robot 3D Embodied Artificial Intelligence and Autonomous Manipulation

Abstract

Embodied AI is moving robots from structured environments to open, dynamic real-world settings. Autonomous manipulation is key to measuring intelligence and determining adaptability in manufacturing, home service, and healthcare. Its foundation lies in perception and cognition. Despite progress in humanoid robots and embodied foundation models—such as vision-language-action modeling and end-to-end control—robots still struggle with tasks humans find trivial: fragile visual recognition under challenging lighting, occlusion, or deformation; poor generalization to unseen objects/scenes; and limited capability for long-horizon or non-rigid manipulation. This report addresses these challenges and explores new approaches to enhance autonomous manipulation in embodied AI systems.