上海市电站自动化技术重点实验室（Shanghai Key Laboratory of Power Station Automation Technology）
上海大学网络化控制研究中心（Networked Control Reserch Center, Shanghai University）
论坛时间： 2020年08月31日 下午 2:00-6:00
参与方式：腾讯会议ID: 818 193 428
报告题目：Distributed Cooperative Control of Multiagent Systems: Theory and Applications
报 告 人：Prof.Hamid Reza Karimi（意大利米兰理工大学）
报告摘要:From both theoretical and practical aspects, the problem of distributed cooperative control design for multiagent systems has received increasing attentions in recent years due to its advantages, compared with the traditional centralized systems, including more flexibility, decentralization, stronger robustness. Some practical research impacts could be utilization of multiagent systems in intelligent manufacturing (Industry 4.0), structural control systems, emergency patient transportation, robotics, for instance.
The objective of this talk is to present some challenges and recent results on distributed cooperative control systems or distributed model predictive control (DMPC) of multiagent systems for consensus, e.g. robotics, with a focus on advanced controller design strategy developments under communication control protocol. Specifically, development of the output-feedback consensus control is proposed for heterogeneous linear multi-agent systems in presence of disturbance and nonuniform sampling process, moreover, joint design of self-triggered mechanism and DMPC is addressed for unconstrained linear multi-agent systems. The talk will be concluded with some concluding remarks on both technical and practical aspects of distributed control systems for consensus problems of multiagent systems.
报告人简介：Professor Hamid Reza Karimi is currently Professor of Applied Mechanics with the Department of Mechanical Engineering, Politecnico di Milano, Milan, Italy. From 2009-2016, he has been Full Professor of Mechatronics-Control Systems at University of Agder, Norway.His current research interests include control systems and mechatronics with applications to automotive control systems, vibration systems and wind energy.
Prof. Karimi is currently theEditor-in-Chiefof the Journal of Cyber-Physical Systems,Editor-in-Chiefof the Journal of Machines,Editor-in-Chiefof the International Journal of Aerospace System Science and Engineering,Editor-in-Chiefof the Journal of Designs,SectionEditor-in-Chiefof the Journal of Electronics, SectionEditor-in-Chiefof the Journal of Science Progress, Subject Editorfor Journal of The Franklin Institute and a Technical Editor, Moderator for IEEE TechRxiv or Associate Editor for some international journals, for instance, the IEEE Transactions on Fuzzy Systems, the IEEE Transactions on Neural Networks and Learning Systems, the IEEE Transactions on Circuits and Systems-I: Regular Papers, the IEEE/ASME Transactions on Mechatronics, the IEEE Transactions on Systems, Man and Cybernetics: Systems, Information Sciences, IFAC-Mechatronics, International Journal of Robust and Nonlinear Control. He is a member of Agder Academy of Science and Letters and also a member of the IEEE Technical Committee on Systems with Uncertainty, the Committee on Industrial Cyber-Physical Systems, the IFAC Technical Committee on Mechatronic Systems, the Committee on Robust Control, and the Committee on Automotive Control. Prof. Karimi has been awarded as the 2016-2019 Web of Science Highly Cited Researcher in Engineering and also received the 2020 IEEE Transactions on Circuits and Systems Guillemin-Cauer Best Paper Award.
报告题目：Learning Control:Ideas and Problems in Adaptive Fuzzy Control
报 告 人：Prof.Shun-Feng Su（台湾科技大学）
报告摘要：Intelligent control is a promising way of control design in recent decades. Intelligent control design usually needs some knowledge of the system considered. However, such knowledge usually may not be available. Learning becomes a important mechanism for acquiring such knowledge. Learning control seems a good idea for control design for unknown or uncertain systems. To learn controllers is always a good idea, but somehow like a dream. It is because learning is to learn from something. But when there is no good controller, where to learn from? Nevertheless, there still exist approaches, such as adaptive fuzzy control, that can facilitate such an idea. It is called performance based learning (reinforcement learning and Lyapunov stability). This talk is to discuss fundamental ideas and problems in one learning controller -- adaptive fuzzy control. Some deficits of such an approach are discussed. The idea is simple and can be extended to various learning mechanisms. In fact, such an idea can also be employed in various learning control schemes. If you want to use such kind of approaches, those issues must be considered in your study.
报告人简介：ProfessorShun-Feng Su received the B.S. degree in electrical engineering, in 1983, from National Taiwan University, Taiwan, R.O.C., and the M.S. and Ph.D. degrees in electrical engineering, in 1989 and 1991, respectively, from Purdue University, West Lafayette, IN.
He is now a Chair Professor of the Department of Electrical Engineering, National Taiwan University of Science and Technology, Taiwan, R.O.C. He is anIEEE Fellow,IFSA fellow,CACS fellowandRST fellow. He has published more than 300 refereed journal and conference papers in the areas of robotics, intelligent control, fuzzy systems, neural networks, and non-derivative optimization. His current research interests include computational intelligence, machine learning, virtual reality, intelligent transportation systems, smart home, robotics, and intelligent control.
Prof. Su is very active in various international/domestic professional societies. He now is the IEEE SMC society Distinguished Lecturer Program chair. He also serves as a board member of various academic societies. Prof. Su also acted as General Chair, Program Chair, or various positions for many international and domestic conferences. Prof. Su currently serves asAssociate editorsof IEEE Transactions on Cybernetics, IEEE/CAA Journal Automatica Sinca and IEEE Access, a subject editor (Electrical Engineering) of the Journal of the Chinese Institute of Engineers, and theEditor-in-Chiefof International Journal of Fuzzy Systems.
报告题目：Computation Offloading for Mobile Edge Computing in IoT Networks
报 告 人：Prof.Yu-Chu Tian（澳大利亚昆士兰科技大学）
报告摘要：Mobile edge computing evolves quickly as a tool for alleviating resource limitations in IoT Networks through computation offloading. In computation offloading to edge servers, it is important to design an offloading strategy with low latency, minimum energy consumption, and enhanced security in an environment of multiple users and multiple tasks. This talk will report our recent progress in this area. We embed the computation offloading problem into an optimization framework. Due to its nonlinear feature and NP hardness, the optimization problem is approximated to a linear representation for practical solving. Simulation results will be presented to demonstrate our approach.
报告人简介：ProfessorYu-Chu Tian, computer scientist and Australia Research Council (ARC) recognized expert of international standing. He is a professor of computer science at the School of Electrical Engineering and Computer Science at Queensland University of Technology in Australia. Prof Tian received the PhD degree from Zhejiang University in 1993 and the PhD degree from the University of Sydney in 2009. He worked in a number of universities in the mainland of China, Hong Kong, USA and Australia. Since 2002, he has been with Queensland University of Technology in Brisbane, Australia, initially as a Lecturer and Senior Lecturer, later as an Associate Professor and Head of Discipline of Networks and Communications, and currently as a Professor. He has published a monograph and over 200 refereed papers. His current research interests include big data, distributed computing and cloud computing, computer networks, real-time systems, systems engineering, and control theory and engineering. He is theeditor-in-chieffor Springer’s Handbook of Real-Time Computing, an Associate Editor for a few international journals.