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报 告 人：林宗利，弗吉尼亚大学教授
报告题目：Co-Design of Linear Low-and-High Gain Feedback and High Gain Observer for
Suppression of Effects of Peaking on Semi-Global Stabilization
内容简介：A globally asymptotically stable nonlinear system cascaded by a single input single output linear system through its output has been shown to be semi-globally asymptotically stabilizable by low-and-high gain feedback of the state of the linear system if the linear system is controllable and with its invariant zeros located at the origin. Such low-and-high state feedback can be implemented by an observer. To retain a domain of attraction arbitrarily close to the domain of attraction under a given state feedback, high gain observer is employed to achieve arbitrarily fast decay of the observation errors of all states and the effect of the peaking phenomenon associated with the high observer gain is overcome by saturating the control input outside a region that contains the desired domain of attraction. In this talk, we present a co-design of the linear low-and-high gain state feedback and the high gain observer for semi-global stabilization of such a cascaded system without resorting to saturating the control input. Moreover, our design does not rely on making all state observation errors decay to zero arbitrarily fast.
报告人简介：Zongli Lin is the Ferman W. Perry Professor in the School of Engineering and Applied Science and a Professor of Electrical and Computer Engineering at the University of Virginia. He received his B.S. degree in Mathematics and Computer Science from Xiamen University, Xiamen, China, in 1983, his Master of Engineering degree in automatic control from Chinese Academy of Space Technology, Beijing, China, in 1989, and his Ph.D. degree in electrical and computer engineering from Washington State University, Pullman, Washington, in 1994. His current research interests include nonlinear control, robust control, and control applications. He is a Fellow of IEEE, IFAC, AAAS and CAA.
报 告 人：陈翔，温莎大学教授
报告题目：Multi-objective H2 and H∞ Filtering and Control--A New Paradigm
内容简介：A new design paradigm is discussed in this talk which allows multi-objective filtering and control designs to achieve complement H2 and H∞ performance with little trade-off. In particular, a revisit of Youla-Kucera parameterization of all stabilizing controllers and the traditional mixed H2/H∞ filtering and control are first presented. Then the new design paradigm is introduced in comparison with the traditional structure. It is shown that the new paradigm is not only able to automatically render the H2 control performance if there is no modeling mismatch for the plant, but also provide recovery, instead of compromise, of the optimal performance when the modeling error is present, noting that the compromise is normally seen in traditional mixed designs. It is also noted that the recovery of the robust performance is regulated by the ‘measured error size’ of the modeling mismatch, hence, resulting in less conservativeness of the control performance. An inverted pendulum example is presented to validate the design expectations of the new control paradigm.
报告人简介：Xiang Chen received M. Sc. and Ph. D. degree in system and control from Louisiana State University in 1996 and 1998. He held cross-appointed positions in Department of Electrical and Computer Engineering and Department of Mechanical, Automotive and Materials Engineering at the University of Windsor, Canada, and is currently a Professor in the Department of Electrical and Computer Engineering. He has made fundamental contribution to Gaussain filtering and control, control of nonlinear systems with bifurcation, networked control system, and optimization of field sensing network. He has also made significant contribution to industrial applications of control and optimization in automotive systems and in visual sensing systems for manufacturing through extensive collaborative research and development activities with automotive, robotics, and manufacturing industries. Some of the deliverables have been patented by relevant companies or transferred to technological products of relevant companies. He is currently a Senior Editor for the IEEE/ASME Transactions on Mechatronics, an Associate Editor for SIAM Journal on Control and Optimization, and Associate Editors for International Journal of Intelligent Robotics and Applications, Control Theory and Technology (English Version), and Unman Systems. He received the Award of Best Paper Finalist from 2017 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM 2017), the Award of Best Student Paper Finalist (as the supervisor author) from 2015 ASME DSCC, the New Opportunity Awards from the Canadian Foundation of Innovation (CFI) and from the Ontario Centre of Excellence-- Materials and Manufacturing Ontario, as well as 4 times Research Awards from the University of Windsor. His research has been well supported by government agencies at both federal and provincial levels in Canada and from industrial companies in both Canada and USA. His current research interests include multi-objective complementary optimization and control of systems with complexities, optimization and control of field sensing network and field sensor based autonomous operations, graph-/game-theoretic approaches for complex networked systems, as well as control applications to automotive systems and autonomous vehicles. He is a registered Professional Engineer in Ontario, Canada.
报 告 人：姜钟平，纽约大学教授
报告题目：Reinforcement Learning and Optimal Control for Uncertain Systems
内容简介：Entanglement of reinforcement learning and control theory has led to tremendous progresses in data-driven control over the past few years. However, most of existing results focused more on problems with stationary models plus infinite horizon costs, which leads to stationary value functions and stationary optimal controls. Relatively few results are known for problems with time-varying models plus finite or infinite horizon costs, which leads to time-varying value functions and time-varying optimal controls. Due to the fundamental difference between these two kinds of problems, the methods for the stationary case can hardly be adopted for the nonstationary setting directly. In this talk, I will present our recent results in learning-based optimal control for uncertain systems which may be time-varying. If time permits, I will also report on our latest work that looks at the robustness analysis of reinforcement learning algorithms from a nonlinear control perspective.
报告人简介：Zhong-Ping Jiang received the M.Sc. degree in statistics from the University of Paris XI, France, in 1989, and the Ph.D. degree in automatic control and mathematics from the Ecole des Mines de Paris (now, called ParisTech-Mines), France, in 1993, under the direction of Prof. Laurent Praly. Currently, he is a Professor of Electrical and Computer Engineering at the Tandon School of Engineering, New York University. His main research interests include stability theory, robust/adaptive/distributed nonlinear control, robust adaptive dynamic programming, reinforcement learning and their applications to information, mechanical and biological systems. In these fields, he has written five books and is author/co-author of over 450 peer-reviewed journal and conference papers. Dr. Jiang has served as Deputy Editor-in-Chief, Senior Editor and Associate Editor for numerous journals. Prof. Jiang is a Fellow of the IEEE, a Fellow of the IFAC, a Fellow of the CAA and is among the Clarivate Analytics Highly Cited Researchers.
报 告 人：黄彪，阿尔伯塔大学教授
报告题目：Robustness in Data Analytics
内容简介：Modern industry is awash with a large amount of data. While useful information may be buried in some of data waiting for discovery, others may simply be noises. Extraction of information and knowledge discovery from data, particularly from day by day routine process operating data, is challenging. There are numerous issues such as data nonlinearity, non-Gaussian, high dimensionality, collinearity, multiple modes, outliers, missing measurement etc that must be considered during the information extraction process. This presentation will focus on one of the most important issues in Data Analytics from theoretical perspective, namely the robustness. The presentation introduces probabilistic approaches to coping with robustness issues in data analytics.
报告人简介：Biao Huang received his Ph.D. degree in Process Control from the University of Alberta, Canada, in 1997. He held MSc degree (1986) and BSc degree (1983) in Automatic Control from the Beijing University of Aeronautics and Astronautics. He is currently a Professor with the University of Alberta, IEEE Fellow and Fellow of the Canadian Academy of Engineering. He is the Editor-in-Chief for IFAC Journal Control Engineering Practice, Subject Editor for Journal of the Franklin Institute, and Associate Editor for Journal of Process Control.
报 告 人：陈通文，阿尔伯塔大学教授
报告题目：Advanced Alarm Analytics Tools Developed at the University of Alberta
内容简介：In operating industrial facilities, alarm systems are configured to notify operators about any abnormal situation. The industrial standards (EEMUA and ISA) suggest that on average an operator should not receive more than six alarms per hour. This is, however, is rarely the case in practice as the number of alarms each operator receives is far more than the standard. This talk will summarize some recent results on advanced alarm analytics and present a new set of tools for design of alarm systems and improvement of alarm management. The essential functionalities of the tools include alarm visualization, alarm performance evaluation and analysis, rationalization design, and alarm flood analysis, thereby to help industrial processes to comply with the new standards. The tools have been tested with real industrial data and used by process engineers in Canada and elsewhere.
报告人简介：Tongwen Chen is currently a Professor and Tier 1 Canada Research Chair in Intelligent Monitoring and Control at the University of Alberta, Canada. He received the BEng degree in Automation and Instrumentation from Tsinghua University (Beijing) in 1984, and the MASc and PhD degrees in Electrical Engineering from the University of Toronto in 1988 and 1991, respectively. His research interests include computer and network based control systems, event triggered control, process safety and alarm systems, and their applications to the process and power industries. He is a Fellow of IEEE, IFAC, as well as Canadian Academy of Engineering.