Speech Title: The Adaptive Distributed Observer for an Unknown Leader System and Its Applications

Abstract: Multi-agent control systems arise from numerous engineering applications such as synchronized motion of multiple robots, formation flying of multiple fight vehicles, mobile sensor area coverage, control of power grids, etc. It has been a central control problem for over a decade. A typical multi-agent system contains a follower system consisting of multiple subsystems called followers and a leader system that provides the reference signals to the followers. What distinguishes the control of multi-agent systems from the conventional control for a single system is that the control law for multi-agent systems must be distributed in order to satisfy communication constraints. A distributed observer for a leader system is a distributed dynamic compensator capable of estimating and transmitting the leader's signal to every follower over the communication network of a multi-agent system and is an effective tool for dealing with various leader-following cooperative control problems of complex multi-agent systems. It was first developed in 2010 for a known linear leader system assuming that every follower knows the dynamics of the leader, and then was enhanced in 2016 to the so-called adaptive distributed observer for a known leader system that only requires the children of the leader knows the dynamics of the leader. Since 2017, efforts were made to develop the adaptive distributed observer for an unknown leader system. Such a distributed observer not only estimates the leader’s signal but also the unknown parameters of the leader. In this talk, after an overview of the status of the distributed observer, we will report our on-going efforts on developing an output-based adaptive distribute observer for an unknown linear leader system over jointly connected communication networks. Extensions, variants and applications of the adaptive distributed observer will also be highlighted.

Biography: Jie Huang studied Power Engineering at Fuzhou University from 1977 to 1979 and Circuits and Systems at Nanjing University of Science and Technology (NUST) from 1979 to 1982 for a Master degree. He completed his Ph.D. study in automatic control at Johns Hopkins University in 1990. After a year with Johns Hopkins University as a postdoctoral fellow and four years with industry in USA, he joined the Department of Mechanical and Automation Engineering, the Chinese University of Hong Kong (CUHK) in September 1995, and is now Choh-Ming Li Research Professor of Mechanical and Automation Engineering. He was a “State Specially Recruited Expert” of China, served as a Science Advisor to the Leisure and Cultural Services Department of Hong Kong Special Administrative Region, Honorary Advisor to Hong Kong Science Museum, and Chairman of the Department of Mechanical and Automation Engineering, CUHK. His research interests include nonlinear control, networked multi-agent systems control, game theory, and guidance and control of flight vehicles. He has authored/co-authored four monographs and over 400 papers.
Jie Huang has received several awards such as Outstanding Contribution Award by Technical Committee of Control Theory, China Association of Automation, in 2015, China State Natural Science Award, second prize, in 2010, Croucher Senior Research Fellowship award in 2006, and Changjiang Professor Award in 2002. He was elected HKIE Fellow in 2017, CAA Fellow in 2010, IFAC Fellow in 2009, and IEEE Fellow in 2005, and is now a life fellow of IEEE. <personal webpage >

Speech Title: TBA

Abstract: TBA

Biography: Lihua Xie received the B.E. and M.E. degrees in electrical engineering from Nanjing University of Science and Technology in 1983 and 1986, respectively, and the Ph.D. degree in electrical engineering from the University of Newcastle, Australia, in 1992. Since 1992, he has been with the School of Electrical and Electronic Engineering, Nanyang Technological University (NTU), Singapore, where he is currently Professor and Director of $25M National Research Foundation Medium Sized Centre for Advanced Robotics Technology Innovation (CARTIN) and Co-Director, Delta-NTU Corporate Laboratory for Cyber-Physical Systems which received $45M funding from National Research Foundation of Singapore, Delta Electronics and NTU, and has over 100 researchers including 20 professors, 51 research staff and 30 PhD students. He served as the Head of Division of Control and Instrumentation from July 2011 to June 2014 and the Director, Center for E-City from July 2011 to June 2013. He held teaching appointments in the Department of Automatic Control, Nanjing University of Science and Technology from 1986 to 1989.
Dr. Xie’s research interests include robust control, networked control systems, multi-agent networks, indoor positioning, human activity recognition and unmanned systems. He has published 9 books, over 500 journal papers, 380 conference papers, and 20 patents/Technical Disclosures. He has been listed as a highly cited researcher (SCI: 27400, H-index: 79; Google Scholar: 44,600, H-index: 103) by Thomson Routers and Clarivate Analytics annually since 2014. He has secured a total research grant of over 90 million Singapore dollars as programme and project PI, and graduated 38 PhD students. He has received many awards for his research including IBM Faculty Award, Changjiang Scholar Award from Ministry of Education of China, and best paper awards at 8 international conferences such as the Guan Zhao Zhi Award from the 29th and 39th Chinese Control Conference (CCC) both of which had over 2000 participants, 7th Asia Control Conference (ASCC), 18th International Conference on Advanced Robotics (ICAR 2017), etc. <personal webpage >

Speech Title: From ChatGPT to Domestic Multimodal Fundamental Large Models

Abstract: With the rapid development of deep learning technologies, ChatGPT, as a significant breakthrough in the field of natural language processing, has garnered widespread attention. This presentation explores the importance of ChatGPT in natural language processing and its exceptional features. Subsequently, we delve into the significance of developing domestic multimodal foundational large models and how to construct new domestic foundational models by combining multimodal data with deep learning technologies. Specifically, we will discuss the processing methods for multimodal data, key technologies for model construction, and how to achieve compact deployment, among other related technical aspects. The research results of this lecture will provide references for solving deployment challenges of multimodal large models and enhancing model performance and efficiency.

Biography: R. Ji is a Nanqiang Distinguished Professor at Xiamen University, the Deputy Director of the Office of Science and Technology at Xiamen University, and the Director of Media Analytics and Computing Lab. He was awarded as the National Science Foundation for Excellent Young Scholars (2014), the National Ten Thousand Plan for Young Top Talents (2017), and the National Science Fundation for Distinguished Young Scholars (2020). His research falls in the field of computer vision, multimedia analysis, and machine learning. He has published 50+ papers in ACM/IEEE Transactions, including TPAMI and IJCV, and 100+ full papers on top-tier conferences, such as CVPR and NeurIPS. His publications have got over 20K citations in Google Scholar. He was the recipient of the Best Paper Award of ACM Multimedia 2011. He has served as Area Chairs in top-tier conferences such as CVPR and ACM Multimedia. He is also an Advisory Member for Artificial Intelligence Construction in the Electronic Information Education Commitee of the National Ministry of Education.