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特邀青岛大学侯忠生教授、东北大学王占山教授、清华大学张靖副教授来校作学术报告
发布日期:2019-10-12

报告题目1PID and Its Puzzles——MFAC and Progress

报告人:侯忠生 教授

 

报告题目2Stability Analysis of Neural Dynamical Networks with Time-Delays

报告人:王占山 教授

 

报告题目3基于硅芯片上光学微腔的光机械非线性及其在微纳传感的应用

报告人:张靖 副教授

 

报告时间:1012日(周六)下午2:30-4:30

报告地点:学科3号楼S410会议室

主持人:李涛 教授

欢迎广大师生踊跃参加!

江苏省大数据分析技术重点实验室

江苏省气象能源利用与控制工程技术研究中心

江苏省大气环境与装备技术协同创新中心

自动化学院

20191012

Abstract 1: Many practical processes generate and store a huge amount of process data, which contains all the valuable information of the process operations and the equipment. How to use these process data, both on-line and off-line, to directly determine the controller structure, tune the controller parameter, design the output prediction, make the performance assessment, etc., would have great significance when the process models are unavailable. Therefore, the establishment on the data driven control theory is an urgent and important issue both for the theoretical development and field applications of the control theory.This talk includes four parts. The first is a brief survey on the existing problems of PID controller; the second is the dynamic linearization data modeling method for nonlinear systems; The third part will present the model free adaptive control (MFAC), including the indirect MFAC, the direct MFAC, and its progress; The final one is the MFAC application to a benchmark problem.

Abstract 2 :Neural dynamical networks are a kind of optimal model, which can be used to solve a class of optimization problems. In general, the stable equilibrium point of optimal model corresponds to the optimal solution of the concerned problem. Therefore, how to establish some sufficient conditions for the existence and stability of equilibrium point of the neural dynamical networks is a fundamental problem. Neural networks with time delay can bring some new insights for the applications of optimal problems. Therefore, how to establish the stability conditions and reduce the conservativeness of the existing result has become a research branch of academic community. In this report, how to use the information on time delay both in the construction of Lyapunov-Krasovskii functional (LKF) and calculation of the derivative of LKF will be introduced. Especially, the delay decomposition method, flexible terminal method and multiple-integral based LKF method will be mainly introduced for the neural networks with time delays. Then the relationship between stability and synchronization/consensus will be discussed for the collective dynamics of interconnected neural dynamics, in which one can find the evolutionary process of static analysis of a dynamical system.  

报告摘要3:报告内容分成三个部分:(1) 简单介绍我们所提出的量子反馈非线性化的概念,及其在基于光学微腔的可调声子激光,微位移探测等方面的应用与实验进展;(2) 简单介绍我们在硅芯片上微型环芯腔系统中所理论分析以及实验观测到的分岔、混沌以及随机共振等非线性现象,并进一步介绍其在微纳传感的应用;(3) 简单介绍微型环芯腔在微纳传感方面的其他应用。

报告人简介1Zhongsheng Hou (SM’13) 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 1995 to 1997, he was a Postdoctoral Fellow with Harbin Institute of Technology, Harbin, China. From 2002 to 2003, he was a Visiting Scholar with Yale University, CT, USA. 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 the School of Automation, 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. Up to now, he has authored or co-authored more than 180 peer-reviewed journal papers and over 140 papers in prestigious conference proceedings. 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 160 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. His works on data-driven learning and control has been supported by multiple projects supported by the National Natural Science Foundation of China (NSFC), including three Key Projects in 2009, 2015, and 2019, respectively, and a Major International Cooperation Project in 2012.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. He is also an International Federation of Automatic Control Technical Committee Member of both “Adaptive and Learning Systems” and “Transportation Systems.”  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.   

报告人简介2Zhanshan Wang is acurrently in the College of Information Science and Engineering, Northeastern University, Shenyang, China. He received the Ph.D.degree in control theory and control engineering from the Northeastern University, Shenyang, China, in 2006.Since 2010,he has been a Professor with Northeastern University. He has authored and coauthored morethan150journaland conference papers and 6 monographs. He is the holder of ten Chinese patents. His research interesting in clude stability theory, neural networks theory, learning control, fault diagnosis, fault tolerant control, nonlinear control theory, and their applications in smart grid. He received the Excellent Doctoral Dissertation Tutor Award of Chinese Association of Automation in 2018,the Nomination of 100 Excellent Doctoral Dissertation in China in 2009,the Excellent Doctoral Dissertations in Liaoning Province in 2008.He was elected to Ministry of Education's Supporting Plan for Excellent Talents in the New Century in 2010 and the Excellent Postdoctoral Students in Liaoning Province in 2010.He was an Associate Editor for IEEE Transaction on Neural Networks and Learning Systems. He is currently a member of the editorial board of Acta Automatica Sinica.

报告人简介3:张靖,清华大学自动化系副教授,博导,20017月本科毕业于清华大学数学科学系,20019月推荐到清华大学自动化系攻读博士学位,在清华大学自动化系李春文教授和美国华盛顿大学谈自忠教授的共同指导下从事量子控制的研究,20067月获得博士学位。20067月至20085月在清华大学计算机系做博士后。20085月博士后出站后,在清华大学自动化系工作至今。主要研究兴趣包括:(1) 硅基微纳光子学实验;(2) 量子控制理论。张靖作为第一作者于2011年获国际自动控制联合会(IFAC)世界大会青年作者奖,是我国高校系统学者第一次获此奖项,该会议是国际自动控制领域规模和影响最大的会议,该奖项每三年评给一篇论文。2012年,入选清华大学基础学科青年人才支持计划(221计划)。2016年得到国家自然科学基金优秀青年基金支持。

 


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