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系统科学学科建设系列专家讲座:走进青岛大学系统科学学科

讲座编号:jz-yjsb-2021-y054

讲座题目:系统科学学科建设系列专家讲座:走进青岛大学系统科学学科

人:

侯忠生 教授、博导 青岛大学自动化学院

林崇 教授、博导 青岛大学自动化学院

车伟伟 教授、博导   青岛大学自动化学院

讲座时间:20211211日(星期六)上午09:00

讲座地点:腾讯会议(会议号:429-293-791

参加对象:IM体育系统科学研究院、人工智能学院全体教师和研究生

主办单位:IM体育系统科学研究院、人工智能学院、研究生院

主讲人简介:

Zhongsheng Hou (SM’13, F’20) received the B.S. and M.S. degrees from Jilin University of Technology, Jilin, China, in 1983 and 1988, respectively, and the Ph.D. degree from Northeastern University, Shenyang, China, in 1994. From 1997 to 2018, he was with Beijing Jiaotong University, Beijing, China, where he was a Distinguished Professor and the Founding Director of Advanced Control Systems Lab, and the Head of the Department of Automatic Control. He is currently a Chair Professor with Qingdao University, Qingdao, China. His research interests are in the fields of data-driven control, model-free adaptive control, learning control, and intelligent transportation systems. He has authored two monographs, Nonparametric Model and its Adaptive Control Theory, Science Press (in Chinese), 1999, and Model Free Adaptive Control: Theory and Applications, CRC Press, 2013. His pioneering work on model-free adaptive control has been verified in more than 200 different field applications, laboratory equipment and simulations with practical background, including wide-area power systems, lateral control of autonomous vehicles, temperature control of silicon rod.

Prof. Hou is the Founding Director of the Technical Committee on Data Driven Control, Learning and Optimization (DDCLO), Chinese Association of Automation (CAA), and is a Fellow of CAA. Dr. Hou was the Guest Editor for two Special Sections on the topic of data-driven control of the IEEE Transactions on Neural Networks in 2011, and the IEEE Transactions on Industrial Electronics in 2017.

林崇,青岛大学二级教授,山东省泰山学者。1999年南洋理工大学获博士学位;曾在香港大学、新加坡国立大学、英国布鲁奈尔大学、约翰内斯堡大学做研究工作;2006年至今青岛大学复杂性科学研究所从事教学科研工作。出版合作专著2,合作发表SCI检索论文160余篇。主持国家级、省部级科研项目6项,参与多项。获省部级自然科学奖4项。2014年至今每年入选爱思唯尔中国高被引学者榜单,2018年至今每年入选科睿唯安“高被引科学家”名单。IEEE高级会员;担任多本国内外学术期刊的编委,如IJSSJFI,《控制与决策》,《复杂系统与复杂性科学》等。

车伟伟,女,19804月生,青岛大学自动化学院教授,博士,博士研究生导师。20087月获得东北大学导航、制导与控制专业博士学位,200810月至200910月于新加坡南洋理工大学做博士后(Research Fellow),20151月至20154月在香港大学做访问学者。20173月至20178月在国家自然科学基金委信息学部三处兼聘。山东省泰山学者青年专家计划。辽宁省百千万工程“千层次”;现为国际SCI杂志International Journal of Fuzzy Systems副主编。主持国家自然科学基金项目3项、主持国家自然科学基金联合重点项目子课题1项、山东省重点项目1项,主持其它余省部级课题10余项;以第一作者及通信作者发表SCI论文50余篇。

主讲内容:

How to design a control system with ability of utilizing data and knowledge?”讲座:Professor R. E. Kalman was the founder and visionary leader of the field in modern control theory. His influence transcends well beyond system and control into diverse fields of engineering, mathematics, and others. However, there have been huge significant developments in science, engineering, technology, and society in the last few decades. It is clear that change will accelerate further in the coming decades. Thus, thinking about the relevance and framework of the control theory in post-Kalman under big data, IIoT or AI age, that might illuminate the path of the system and control research for the future. This talk includes five parts. Background of big data/IIoT/AI; Kalman’s Paradigm and its Challenges; Model free adaptive control (MFAC) and its ability of utilizing data and knowledge; Relationships between MFAC with adaptive control and PID; and Conclusion.

“几类状态空间系统的稳定性分析及进展”讲座:在系统理论与控制理论领域,以状态空间描述的动态系统依据各类特性可以划分为众多系统类型。本报告针对其中的两类系统,即广义系统和时滞系统,主要介绍系统稳定性分析方法,汇报系统分析与镇定方法的研究进展,探讨扩展性研究问题及应用。

Data-Driven Security Control Against Network Attacks”讲座:In practical systems, the accurate models are usually difficult to obtain with the development of the industrial technology. Therefore, data-driven control methods have attracted more and more attention in the big data era. In addition, while providing convenience, the wireless network channels used to transmit a large amount of system data will be maliciously attacked. Thus, the security problem is very important for data-driven control methods. This report focuses on the data-driven security control problem against two types of denial-of-service attacks for a class of nonlinear systems. At the same time, two kinds of attack compensation mechanism are presented to alleviate the influence of network attacks, respectively.