講座編號：jz-yjsb-2021-y054

講座題目：系統科學學科建設系列專家講座：走進青島大學系統科學學科

主 講 人：

侯忠生 教授、博導 青島大學自動化學院

林崇 教授、博導 青島大學自動化學院

車偉偉 教授、博導   青島大學自動化學院

講座時間：2021年12月11日（星期六）上午09:00

講座地點：騰訊會議（會議號：429-293-791）

參加對象：北京工商大學系統科學研究院、人工智能學院全體教師和研究生

主辦單位：北京工商大學系統科學研究院、人工智能學院、研究生院

主講人簡介：

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，青島大學二級教授，山東省泰山學者。1999年南洋理工大學獲博士學位；曾在香港大學、新加坡國立大學、英國布魯奈爾大學、約翰內斯堡大學做研究工作；2006年至今青島大學復雜性科學研究所從事教學科研工作。出版合作專著2部,合作發表SCI檢索論文160余篇。主持國家級、省部級科研項目6項，參與多項。獲省部級自然科學獎4項。2014年至今每年入選愛思唯爾中國高被引學者榜單，2018年至今每年入選科睿唯安“高被引科學家”名單。IEEE高級會員；擔任多本國內外學術期刊的編委，如IJSS，JFI，《控制與決策》，《復雜系統與復雜性科學》等。

車偉偉，女，1980年4月生，青島大學自動化學院教授，博士，博士研究生導師。2008年7月獲得東北大學導航、制導與控制專業博士學位，2008年10月至2009年10月于新加坡南洋理工大學做博士后（Research Fellow），2015年1月至2015年4月在香港大學做訪問學者。2017年3月至2017年8月在國家自然科學基金委信息學部三處兼聘。山東省泰山學者青年專家計劃。遼寧省百千萬工程“千層次”；現為國際SCI雜志International Journal of Fuzzy Systems副主編。主持國家自然科學基金項目3項、主持國家自然科學基金聯合重點項目子課題1項、山東省重點項目1項，主持其它余省部級課題10余項；以第一作者及通信作者發表SCI論文50余篇。

主講內容：

“How to design a control system with ability of utilizing data and knowledge?”講座：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.

“幾類狀態空間系統的穩定性分析及進展”講座：在系統理論與控制理論領域，以狀態空間描述的動態系統依據各類特性可以劃分為眾多系統類型。本報告針對其中的兩類系統，即廣義系統和時滯系統，主要介紹系統穩定性分析方法，匯報系統分析與鎮定方法的研究進展，探討擴展性研究問題及應用。

“Data-Driven Security Control Against Network Attacks”講座：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.