Prof. Jian Sun
Senior Member, IEEE
Beijing Institute of Technology, China

Speech Title: Data-driven control of networked systems

Abstract: With the development of information technologies, control systems are becoming more intelligent and interconnected. Accurate modeling of a control system has become increasingly difficult. For systems that are difficult to accurately model, traditional model-based control theories and methods are difficult to achieve ideal control performance. Data-driven control refers to the control method of designing controllers based solely on the offline/online data when the mathematical model and parameters of the control system are unknown. Data-driven control methods are independence of precise models and have broad applications. This talk will introduce the recent progress of data-driven control methods for networked systems, including data-driven event-triggered control and self-triggered control, data-driven resilient control under DoS attacks, data-driven self-triggered control based on trajectory prediction, data-driven robust LQG control, and data-driven output regulation of networked systems.

Biography: Jian Sun received the Bachelor’s degree from the Department of Automation and Electric Engineering at Jilin Institute of Technology, Changchun, China, in 2001, the Master’s degree from Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences (CAS), Changchun, China, in 2004, and the Ph.D. degree from Institute of Automation, Chinese Academy of Sciences (CAS), Beijing, China, in 2007. He was a research fellow at Faculty of Advanced Technology, University of Glamorgan, UK, from April 2008 to October 2009. He was a postdoctoral research fellow at Beijing Institute of Technology, Beijing, China, from December 2007 to May 2010. In May 2010, he joined the School of Automation, Beijing Institute of Technology, where he has been a professor since 2013. His current research interests include autonomous unmanned systems, networked control systems, and data-driven control. He is a member of the Editorial Boards of several journals including IEEE Transactions on Systems, Man & Cybernetics: System, Science China Information Sciences, Journal of Systems Science & Complexity and ACTA AUTOMATICA SINICA.

Prof. Junzhi Yu
IEEE Fellow
Peking University, China

Speech Title: Modeling and Control for Collective Intelligence in Underwater Biomimetic Robotic Fish

Abstract: Many organisms in nature show the group attributes of orderly organization, evident division of labor, and mutual cooperation, demonstrating the outstanding collective intelligence, which provides a lot of inspiration for the research of unmanned system clusters. This report focuses on the group modeling and control of unmanned systems based on underwater biomimetic robotic fish. According to the progressive relationship from “learning fish school” to “imitating fish school” to “integrating into fish school”, the representative results of the team in recent years in the direction of imitating fish school intelligence will be shared, involving fish school behavior modeling, cooperative formation control, robotic fish inducting biological fish, and so on. At the end of the report, the development prospect of underwater collective intelligence will be prospected, and the corresponding research plan will be introduced.

Biography: Junzhi Yu (Fellow, IEEE) received the BE degree in safety engineering and the ME degree in precision instruments and mechanology from the North University of China, Taiyuan, China, in 1998 and 2001, respectively, and the PhD degree in control theory and control engineering from the Institute of Automation, Chinese Academy of Sciences, Beijing, China, in 2003. From 2004 to 2006, he was a postdoctoral research fellow with the Center for Systems and Control, Peking University, Beijing. In 2006, he was an associate professor with the Institute of Automation, Chinese Academy of Sciences, where he became a full professor in 2012. In 2018, he joined the College of Engineering, Peking University, as a tenured full professor. His research interests include intelligent robots, motion control, and intelligent mechatronic systems.

Prof. Lixian Zhang
IEEE Fellow
Harbin Institute of Technology, China

Speech Title: In-Cabin Robots for Space Stations: Research Status and Key Technologies

Abstract: The in-cabin robots for space stations, including humanoid robots, crawling robots, and flying robots, are regarded as pivotal tools for advancing space station automation. Since 1998, world-leading space agencies, such as NASA and RKA, have spearheaded the development of in-cabin robots for space stations, with multiple prototypes having successfully undergone on-orbit experimental verification aboard the International Space Station. Compared to Earth-based robots, in-cabin robots working in space stations face technical challenges in terms of highly-integrated configuration design, micro-gravity motion control, and safety-critic human-robot interaction, leading to the emergence of divergent configuration schemes and technical solutions worldwide. This report will provide a systematic analysis of in-cabin robots for space stations, covering their research milestones and key technologies, and projecting their future development in in-cabin environmental monitoring, astronaut-robot collaboration, and on-orbit swarm operations.

Biography: Lixian Zhang received the Ph.D. degree in control science and engineering from the Harbin Institute of Technology (HIT), Harbin, China, in 2006. From January 2007 to September 2008, he was a Postdoctoral Fellow in the Department Mechanical Engineering at the Ecole Polytechnique de Montreal, Canada. He was a Visiting Professor at the Process Systems Engineering Laboratory, Massachusetts Institute of Technology (MIT), Cambridge, MA, USA, from February 2012 to March 2013. Since January 2009, he has been with the Harbin Institute of Technology, where he is currently a Full Professor and the Vice Dean of the Institute for Artificial Intelligence of HIT. Prof. Zhang currently serves as Senior Editor for IEEE Control Systems Letters, and previously served as Associate Editor for IEEE Transactions on Automatic Control and IEEE Transactions on Cybernetics. He is a winner of the National Science Fund for Distinguished Young Scholars, and has been honored with the Qian Xuesen Outstanding Contribution Award. He has been listed as a Clarivate Analytics Highly Cited Researcher from 2014 to 2023. He is a Fellow of IEEE and IET.

Prof. Youqing Wang
IET Fellow, The Dean of College of Information Science and Technology
Beijing University of Chemical Technology, China

Speech Title: Theoretical Methods and Industrial Applications of Clustering Analysis

Abstract: Clustering analysis is a representative unsupervised method in machine learning, which attracts widespread attention in many practical fields due to its independence from artificial label information. However, unsupervised setting and increasingly complex data (such as graph data, large-scale multi-view data, etc.) bring significant challenge to the effectiveness of clustering. This presentation would focus on introducing self-supervised information enhanced graph contrastive clustering and large-scale fast multi-view subspace clustering methods, exploring how to mine and utilize data correlations to enhance the clustering process. We further explore the remaining issues and potential industrial applications of clustering analysis.

Biography: Youqing Wang received the B.S. degree in Mathematics from Shandong University, Jinan, Shandong, China, in 2003, and PhD degree in Control Science and Engineering from Tsinghua University, Beijing, China, in 2008. He worked chronologically at Hong Kong University of Science and Technology, Hong Kong, China; University of California, Santa Barbara, USA; University of Alberta, Edmonton, Canada; Shandong University of Science and Technology, Qingdao, China; City University of Hong Kong, Hong Kong, China. He is currently a professor and the dean of College of Information Science and Technology, Beijing University of Chemical Technology, Beijing, China. His research interests include fault-tolerant control, state monitoring, iterative learning control, and their applications on chemical processes. Dr. Wang was a recipient of several research awards, including the NSFC Distinguished Young Scientists Fund, IET Fellow, the Journal of Process Control Survey Paper Prize, and ADCHEM2015 Young Author Prize. Dr. Wang is (was) the editorial member for nine SCI journals and he is also the member of three technical committees of International Federation of Automatic Control (IFAC).

Prof. Qinglai Wei
Senior Member, IEEE
Chinese Academy of Sciences, China

Speech Title: Self-learning Optimal Control and Reasoning System

Abstract: This talk mainly introduces the basic principle and research progress of self-learning control method for nonlinear systems based on adaptive dynamic programming (ADP). Adaptive dynamic programming was first proposed by American scholar P. J. werbos. Based on the optimality principle and the advanced method of integrating artificial intelligence, it is a method to solve the intelligent optimal control problem of large-scale complex nonlinear systems. Adaptive dynamic programming is based on the principle of enhanced learning, uses the nonlinear function fitting method to approximate the performance index of dynamic programming, simulates the idea of human learning through environmental feedback, and effectively solves the problem of "dimension disaster" of dynamic programming. In recent years, it is considered to be a learning control method very close to human brain intelligence. In this talk, a self-learning optimal parallel control by ADP method is proposed, the criteria of the self-learning control are presented. Finally, an introduction of the Reasoning System is developed.

Biography: Qinglai Wei received the B.S. degree in Automation, and the Ph.D. degree in control theory and control engineering, from the Northeastern University, Shenyang, China, in 2002 and 2009, respectively. From 2009--2011, he was a postdoctoral fellow with The State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China. He is currently a professor of the institute and the associate director of the State Key Laboratory. He has authored four books, and published over 80 international journal papers. He was a recipient of the Outstanding Paper Award of IEEE Transactions on Neural Network and Learning Systems and IEEE System, Man, and Cybernetics Society, Andrew P. Sage Best Transactions Paper Award. He was Associate Editors of 6 IEEE Transaction journals. His research interests include parallel control, adaptive dynamic programming, computational intelligence, neural-networks-based control, optimal control, nonlinear systems and their industrial applications.

Prof. Yonggang Zhang
Chief Scientist of the National Key Research and Development Program
Director of Heilongjiang Engineering Laboratory of Navigation Instruments
Deputy Director of Navigation Instrument Engineering Center of the Ministry of Education
Excutive Dean of the College of Future Technology
Harbin Engineering University, China

Speech Title: Intelligent Navigation for Unmanned Systems

Abstract: With the increasingly complex application scenarios of unmanned systems, navigation tasks are facing new challenges such as dynamic environments, complex interference, and no satellite navigation signals. The technical framework in the navigation field is gradually evolving towards multi-source and intelligence. This report introduces how intelligent navigation technology can adapt to the environment, perceive the environment, and integrate swarm intelligence in three application scenarios: underwater autonomous navigation, road network assisted navigation, and cluster relative navigation, in order to improve the performance of autonomous navigation for unmanned systems in complex environments.

Biography: Yonggang Zhang is a Professor of College of Intelligent Systems Science and Engineering, and the Excutive Dean of the College of Future Technology, Harbin Engineering University. He is also the Director of Heilongjiang Engineering Laboratory of Navigation Instruments, deputy Director of Navigation Instrument Engineering Center of the Ministry of Education, and Member of Chinese Society of Inertial Technology. He is the Chief Scientist of the National Key Research and Development Program. His main research areas include navigation technology and information fusion. He has published more than 170 academic papers. He was the recipient of several awards including IEEE Barry Carlton Award, national talent award and Young Scientist Award of the Chinese Society of Automation.