Prof. Xiao He
He Xiao, with the Department of automation, Tsinghua University, is a tenured associate professor, doctoral advisor and deputy head of the Department. The research direction is networked system, fault diagnosis and fault tolerant control. More than 180 papers have been published in domestic and foreign journals and conferences, of which more than 80 have been retrieved by SCI, with more than 1300 times citation in web of science data base. He presided over one key project and two general projects of NSFC, participated in two major projects of NSFC and one major international cooperation project of NSFC, and was funded by excellent youth fund of NSFC in 2015. He is now a senior member of China Association of Automation (CAA), IEEE senior member, sigma Xi full member, and the editorial board member of Control Engineering Practice and other international journals. At present, he is a member of Technical Committee on fault detection, supervision and safety (tc6.4) of IFAC, and Secretary General of Professional Committee on fault diagnosis and safety of CAA. He has won the GIAR award of Sigma Xi - the Scientific Research Society in 2010, Frank best theoretical paper nomination award of SAFEPROCESS International Conference in 2012, the first prize of science and technology progress award of Jilin Province in 2018, and the first prize of Natural Science Award of CAA in 2015 and 2020.
Title: Fault diagnosis technology for brake control system of high-speed trains
Abstract: In order to improve the safety of the brake control system of high-speed railway in China, some key problems of state estimation and fault diagnosis are studied. Aiming at the challenges of non-ideal channels such as bandwidth constraint and data link failure to distributed state estimation, we proposed a series of new distributed filtering methods based on innovation measurement and performance upper bound optimization. These techniques reduce the excessive consumption of bandwidth and energy in existing distributed estimation, and provide a new way for the transmission and utilization of high frequency sampling data. Aiming at the open problem of closed-loop fault diagnosis, we discussed the failure reason of open loop fault diagnosis method in closed-loop system, and an effective improvement method based on historical observation data is proposed and shown. Aiming at the diagnosis bottleneck caused by small amplitude and short duration of intermittent fault, we gave a diagnosability criterion of intermittent fault, and systematic research framework of intermittent fault diagnosis for stochastic dynamic systems is established. Relevant results have been applied to the fault diagnosis of high-speed train brake control system.
Prof. Zhengguang Wu
Zheng-Guang Wu was born in 1982. He received the B.S. and M.S. degrees in mathematics from Zhejiang Normal University, Jinhua, China, in 2004 and 2007, respectively, and the Ph.D. degree in control science and engineering from Zhejiang University, Hangzhou, China, in 2011. He is currently a Professor of Institute of Cyber-Systems and Control, Zhejiang University. His research interests include networked systems, intelligent control, Markov jump systems, smart grid, cyber-physical systems, and reinforcement learning. He has published 100+ papers in IEEE Transactions. He was a recipient of the Highly Cited Researcher Award by Clarivate Analytics. He is an Invited Reviewer of Mathematical Review of the American Mathematical Society. He serves (or has served) as the Associate Editor/Editorial Board Member for some international journals, such as the IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS: SYSTEMS; SCIENCE CHINA Information Sciences; Journal of Systems Science and Complexity; Journal of the Franklin Institute; Neurocomputing; International Journal of Control, Automation, and Systems; IEEE ACCESS; International Journal of Sensors, Wireless Communications and Control; Cyber-Physical Systems; Sensors; Symmetry; and IEEE Control Systems Society Conference Editorial Board.
Title：Stabilization of Boolean Control Networks
Abstract：The purpose of this report is to use some new techniques to discuss the stabilization of Boolean control networks. First, stabilization and finite time stabilization of probabilistic Boolean control networks is investigated. A complete family of reachable sets is defined, based on which, state feedback control stabilization conditions are obtained. Secondly, pinning control is studied to be applied to the Boolean networks to achieve the stabilization control objective. A necessary and sufficient condition is given for the stability of BNs with stochastic disturbances. Thirdly, sampled-data state feedback control with stochastic sampling periods is considered to stabilize Boolean control networks. At last, sampled-data state feedback control with Lebesgue sampling is considered to stabilize Boolean control networks. A necessary and sufficient condition for stabilization is obtained for the considered Boolean control networks.
Prof. Hongyi Li
Guangdong University of Technology
Hongyi Li (SM’17) received the Ph.D. degree in intelligent control from the University of Portsmouth, Portsmouth, U.K., in 2012. He was a Research Associate with the Department of Mechanical Engineering, University of Hong Kong, Hong Kong and Hong Kong Polytechnic University, Hong Kong. He was a Visiting Principal Fellow with the Faculty of Engineering and Information Sciences, University of Wollongong, Wollongong, Australia. He is currently a professor with the Guangdong University of Technology, Guangdong, China. His research interests include intelligent control, cooperative control, sliding mode control and their applications. He was a recipient of the 2016 and 2019 Andrew P. Sage Best Transactions Paper Awards from IEEE System, Man, Cybernetics Society, the Best Paper Award in Theory from ICCSS 2017 and the Zadeh Best Student Paper from IEEE ICCSS 2019, respectively. He has been in the editorial board of several international journals, including IEEE Transactions on Neural Networks and Learning Systems, IEEE Transactions on Fuzzy Systems, IEEE Transactions on Systems, Man and Cybernetics: Systems, IEEE Transactions on Cognitive and Developmental Systems, SCIENCE CHINA Information Sciences, IEEE/CAA Journal of Automatica Sinica, Neural Networks, Asian Journal of Control, Circuits, Systems and Signal Processing, and International Journal of Control, Automation and Systems. He has been Guest Editors of IEEE Transactions on Cybernetics and IET Control Theory and Applications. He is a member of the IFAC Technical Committee on Computational Intelligence in Control.
Title: Cooperative Control and Its Applications of Unmanned Autonomous Systems
Abstract: Unmanned autonomous systems are quite important applications in the artificial intelligence yield. The research of cooperative control has received considerable attention due to extensive applications of unmanned autonomous systems. In this talk, firstly, the background and current research status of cooperative control for unmanned autonomous systems are reported. Then, the main cooperative control problems are addressed for a class of unmanned autonomous systems. Furthermore, the above theories are applied to unmanned autonomous systems. Finally, some future challenges in this area are introduced.
Prof. Quan Quan
Quan Quan received the B.S. and Ph.D. degrees from Beihang University, Beijing, China, in 2004 and 2010, respectively. He was a research fellow in National University of Singapore from June 2011 to October 2011. Since 2013, he has been an Associate Professor with Beihang University, currently with the School of Automation Science and Electrical Engineering. He was also a visiting professor of the University of Toronto in 2017, hosted by Professor W.M. Wonham. His research interests include repetitive control, reliable flight control, and swarm control. He completed the first book about repetitive control for nonlinear systems entitled “Filtered Repetitive Control with Nonlinear Systems.” Also, he published two other books on multicopter systems. He led his group to develop a performance evaluation website flyeval.com for multicopters and a simulation platform RflySim (rflysim.com).
Title: Filtered Repetitive Control with Nonlinear Systems
Abstract. In practice, many control tasks are also often of a periodic nature. For these periodic control tasks, repetitive control (RC, or repetitive controller, also designated RC) can achieve high precision control performance. RC often suffers the robustness problem, including stability robustness against uncertain parameters of systems and performance robustness against uncertain or time-varying period-time of external signals. Filters and frequency domain analysis are the primary tools to solve such a problem, resulting in filtered RCs. But, they can be applied only with difficulty, if at all, to nonlinear systems. This talk aims at providing five methods to explore the potential of RC. Commonly-used methods like the feedback linearization method and adaptive-control-like method will be introduced first. However, feedback linearization or error dynamics derived is often difficult for other various types of problems. To this end, three new methods parallel to the two ways mentioned above will also be shared, which are the additive-state-decomposition based method, the actuator-focused design method, and the contraction mapping method.
Prof. Yalin Wang
Central South University
Yalin Wang is a second-level professor, doctoral supervisor and associate dean at the School of Automation, Central South University, and she is an outstanding talent in the new century of the Ministry of Education. Her current research activity addresses complex industrial process modeling and optimization, industrial big data analysis, intelligent scheduling and optimization decision making. Wang is a member of the IFAC Industry Committee, the Process Control Committee of the Chinese Society of Automation, the Technical Process Fault Diagnosis and Safety Committee of the Chinese Society of Automation, and a vice chairman of the Hunan Society of Automation. She presided over 4 major projects or subjects of the National Science and Technology Plan, 18 other research projects, and participated in more than 20 projects of the National Science and Technology Plan. Won 1 second prize of National Technology Invention Award, 1 second prize of National Science and Technology Progress Award, 6 first prizes and 4 second prizes of provincial and ministerial science and technology awards (including Innovation Team Award, Nature Award, Technology Invention Award, and Science and Technology Progress Award). In the past 5 years, she has published 45 SCI papers as the first or corresponding author, including 3 hot papers and 6 highly cited papers; applied for 42 national invention patents and holds 31.
Title: Online operating performance assessment of hydrocracking process under uncertain information
Abstract: In order to timely adjust its production operations and ensure long-term optimized running, it is of great significance for the hydrocracking process to accurately assess whether its current production deviates from the optimal operating performance and determine the deviation degree. However, due to the harsh detection environment and limited detection technology, it is difficult to detect the key operating performance assessment indicators online. The complexity of the process and data also increases the difficulty of online prediction of these key assessment indicators. Moreover, suffer from three uncertainties of operating acknowledge, data measurement and the information interaction of hierarchical operating structure, the accurate online assessment of operating performance is still difficult. Therefore, driven by big data, we have carried in-depth research on the online operating performance assessment method of hydrocracking process under uncertain information. This report summarizes our relevant research results, and introduces them from the aspects of assessment indicator system construction and modeling preprocessing, online prediction of key assessment indicators, and online assessment of operating performance.