Prof. Tetsuya Iwasaki
University of California, USA
Biography：teD Iwasaki received B.S. and M.S. degrees in Electrical and Electronic Engineering from the Tokyo Institute of Technology in 1987 and 1990, respectively, and a Ph.D. degree in Aeronautics and Astronautics from Purdue University in 1993. He held faculty positions at Tokyo Tech and University of Virginia before joining the University of California, Los Angeles (UCLA), where he is currently Professor of Mechanical and Aerospace Engineering. His research interests include dynamics and control of neuromechanical systems, nonlinear oscillator network, global pattern formation via local interactions, and robust/optimal/distributed control theories and their applications to engineering and medical systems. He has received several awards, including CAREER Award from NSF, Pioneer Prize from SICE, George S. Axelby Outstanding Paper Award from IEEE, and Rudolf Kalman Best Paper Award from ASME. He has served as Senior/Associate Editor of IEEE Transactions on Automatic Control, Systems & Control Letters, IFAC Automatica, International Journal of Robust and Nonlinear Control, and SIAM Journal on Control and Optimization. He is Fellow of IEEE and ASME.ol Systems, Intelligent and Complex Systems, and Power and Energy Systems. He has been named a Highly Cited Researcher by Clarivate annually since 2015.
Speech Title: Distributed Control through Network of Local Stations
Abstract: The classical decentralized control aims at achieving stability and performance through a set of isolated feedback control stations, each of which is responsible for regulation of local variables using local sensors and actuators. Such a decentralized control scheme is required or beneficial for various systems to ensure robustness against local faults and/or to accommodate communication constraints. This talk provides an overview of the classical decentralized control theory, points out the difficulty with the current state of the art, and discusses how the theory can be extended to the more practical notion of distributed control, where local communications are allowed between neighboring control stations. In particular, we show that a plant is stabilizable by a distributed control if and only if the decentralized fixed modes of the plant augmented with the control communication network are stable, and suggest how to determine the control network architecture needed for stabilization. Moreover, we show that the optimal performance of the centralized control can be achieved arbitrarily closely by a distributed control when the control network is strongly connected. The key insights gained by the new theory will be illustrated by a vehicle platoon example.
Prof. Xinghuo Yu
Royal Melbourne Institute of Technology, Australia
Biography：Distinguished Professor Xinghuo Yu specialises in electrical and electronic engineering, and is passionate about making an impact through his fundamental and applied research. He is a Fellow of the Australian Academy of Science, an Honorary Fellow of Engineers Australia, and a Fellow of the IEEE, Australian Computer Society, and Australian Institute of Company Directors. He is also an Engineering Executive and a Chartered Professional Engineer of Engineers Australia.
Distinguished Professor Xinghuo Yu is an Associate Deputy Vice-Chancellor and the Chair of RMIT Professorial Academy. He is a Vice-Chancellor's Professorial Fellow in the School of Engineering. He was the President of IEEE Industrial Electronics Society for 2018 and 2019.
He started his academic career in 1989 as a Postdoctoral Fellow with the University of Adelaide, Adelaide, Australia. In 1991, he joined Central Queensland University, Rockhampton, Australia, where, before he left in 2002, he was Professor of Intelligent Systems and Associate Dean (Research) of Faculty of Informatics & Communication. Since 2002, he has been with RMIT University, where he has occupied various senior academic and administrative positions such as Full Professor, Distinguished Professor, Associate Dean, Institute Director, and Associate Deputy Vice-Chancellor.
He has published extensively in Control Systems, Intelligent and Complex Systems, and Power and Energy Systems. He has been named a Highly Cited Researcher by Clarivate annually since 2015.
Speech Title: Terminal Sliding Modes: From Control to Intelligent Mechatronics
Abstract: Sliding mode control has been studied and utilised extensively due to its robustness and sim-plicity. At its core is the concept of the sliding mode, induced by a discontinuous control law that forces the system state onto a prescribed manifold with desired dynamics. While finite-time reachability of the sliding manifolds is commonly required, conventional sliding mani-folds are typically designed with asymptotic stability.
Terminal sliding mode control as a subclass of SMC has emerged over the past three decades as an effective control strategy, enabling finite-time reachability of both the sliding manifolds and the system equilibrium. This approach offers distinctive advantages such as fast re-sponse, enhanced robustness, and high steady state precision.
In this talk, we will introduce the fundamentals of SMC and TSMC, review their developments, and explore future challenges and opportunities. In particular, we will demonstrate their ap-plications in control systems, intelligent mechatronics, deep learning and global optimisation.
Prof. Jianbin Qiu
Harbin Institute of Technology, China
Biography：Jianbin Qiu received the B.Eng. and Ph.D. degrees in Mechanical and Electrical Engineering from the University of Science and Technology of China, Hefei, China, in 2004 and 2009, respectively. He also received the Ph.D. degree in Mechatronics Engineering from the City University of Hong Kong, Kowloon, Hong Kong, in 2009. He is currently a Full Professor at the School of Astronautics, Harbin Institute of Technology, Harbin, China. He was an Alexander von Humboldt Research Fellow at the Institute for Automatic Control and Complex Systems, University of Duisburg-Essen, Duisburg, Germany. His current research interests include intelligent and hybrid control systems, signal processing, and robotics. Prof. Qiu is a Fellow of IEEE and serves as the chair of the IEEE Industrial Electronics Society Harbin Chapter, China. He is an Associate Editor of IEEE Transactions on Fuzzy Systems, IEEE Transactions on Cybernetics, and IEEE Transactions on Industrial Informatics.
Speech Title: Adaptive Output-Feedback Boundary Control of Distributed Parameter Systems
Abstract: Distributed parameter systems, which are described by partial differential equations, widely exist in aerospace engineering, bioengineering, chemical engineering, and electrical engineering. Over the past decades, the control issues for distributed pa-rameter systems have attracted considerable attention. In particular, the output-feedback adaptive control of distributed parameter systems is very challenging due to limited sensor measurements, unknown spatially varying parameters, and infinite-dimensional coupled dynamics. This talk will introduce some recent results on out-put-feedback adaptive boundary control for several classes of distributed parameter systems. The basic tools include observer canonical form, swapping identifier, and infinite-dimensional backstepping approach.
Prof. Liang Song
Fudan University, China
Biography：Liang Song is currently a Chair Professor with Fudan University, as the director of Fudan Institute on Networking Systems of AI (FINSAI), along with numerous distinguished appointments, e.g., Chairman of Institute on Networking Systems of AI, and the Chairman of Shanghai 5G-VR Alliance, among others. He also sits in board of numerous high technology companies. Prof. Song's work converges communication networks and AI systems, empowering a myriad of industries. His engineering contributions facilitated the continuous upgrading of telecommunications and the Internet, among which he had made key contributions in developing applications and engineering practices of 5GtoB. By laying the foundation for new network infrastructure, his work enables the cross-layer processing of communications, computing and system applications, based on distributed artificial intelligence services. This approach is providing holistic online evolutive learning for real-time AI sensing, control, and generating, becoming an indispensable path for constructing networked AGI. In these technical areas, he has published more than 200 referred papers, 8 monographs, and invented over 100 patents. Due to his significant technical and engineering contributions, he was elected as a Fellow of Canadian Academy of Engineering (FCAE) in 2019, and a Fellow of Chinese National Distinguished Experts in 2013.
Speech Title: On the Scaling Law of Networked AI
Abstract: Networking Systems of AI (NSAI) present the next opportunity to further enhance the efficiency of AI systems. In the talk, we review the recent advances in networked AI with a special focus on the online evolutive learning (OEL), where multiple and heterogenous agent models can interact with each other, with a common language in networking environment. High quality data is acquired online and feed back to optimize all the participating models. Compared to end-to-end large models, OEL can reduce the complexity of model training exponentially by reducing the degrees of freedom, and provide the required scalability for distributed AI systems.
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