报告题目：CONTROL OF CYBER–PHYSICAL–HUMAN NETWORKS
报告人姓名：曹明
报告人单位：荷兰格罗宁根大学（University of Groningen）
报告摘要：Control theory has its roots in the Industrial Revolution, with the centrifugal “fly-ball” governor being one of the first examples of using feedback control to regulate a physical system. The 20th Century brought about a telecommunications revolution, which led to the emergence and proliferation of cyber-physical systems in every aspect of modern society, ranging from industrial control systems to autopilots and distributed computation systems. Cyber-physical-human networks (CPHN) are the next step in this evolutionary journey; increasing digital connectivity means that it is now often critical to consider humans as part of an overall complex engineering system that needs to be modelled, designed, and ultimately, controlled.
CPHN occur in a wide range of application domains, ranging from autonomous robotics to the smart grid and sustainable technologies. The size and scale of CPHN and the role that the human (or humans) play vary significantly, and yet all such systems have complexities which make control and regulation a significant challenge. Engineers and practitioners face both classical and emergent challenges when dealing with CPHN. In order to control and regulate such complex networks, one requires both a suitably accurate model of the system and also robust and reliable control strategies. In this talk, I first review some modeling techniques for some emerging CPHN of increasing research interest, including intelligent traffic systems, smart power grids, epidemic processes and mixed human-robot teams. Then I will show how game theory can play a central role in dealing with the key challenge in modelling and analyzing the human component, involving physiological, cognitive, behavioural dynamics of the human; I will also demonstrate how control actions can be introduced through the human interactions with the cyber-physical part of the system. Executing a control strategy in CPHN typically requires measurement and actuation, and both can be difficult to deal with when it comes to humans; rather, one may employ incentives to guide and influence people, leading to the development of non-traditional control strategies.
个人简历：Ming Cao has since 2016 been a professor of networks and robotics with the Engineering and Technology Institute (ENTEG) at the University of Groningen, the Netherlands, where he started as an assistant professor in 2008. Since 2022 he is the director of the Jantina Tammes School of Digital Society, Technology and AI at the same university. He received the Bachelor degree in 1999 and the Master degree in 2002 from Tsinghua University, China, and the Ph.D. degree in 2007 from Yale University, USA. From 2007 to 2008, he was a Research Associate at Princeton University, USA. He worked as a research intern in 2006 at the IBM T. J. Watson Research Center, USA. He is the 2017 and inaugural recipient of the Manfred Thoma medal from the International Federation of Automatic Control (IFAC) and the 2016 recipient of the European Control Award sponsored by the European Control Association (EUCA). He is an IEEE fellow. He is a Senior Editor for Systems and Control Letters, an Associate Editor for IEEE Transactions on Automatic Control, IEEE Transaction of Control of Network Systems and IEEE Robotics & Automation Magazine, and was an associate editor for IEEE Transactions on Circuits and Systems and IEEE Circuits and Systems Magazine. He is a member of the IFAC Council. His research interests include autonomous robots and multi-agent systems, complex networks and decision-making processes.