Distinguished
Lectures
1.
Prof. Xiao He
Tsinghua University
（何潇，清华大学）
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.
2.
Prof. Zhengguang Wu
Zhejiang University
（吴争光，浙江大学）
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.
3.
Prof. Hongyi Li
Guangdong University of Technology
（李鸿一，广东工业大学）
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.
4.
Prof. Quan Quan
Beihang University
（全权，北京航空航天大学）
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.
5.
Prof. Yalin Wang
Central South University
（王雅琳，中南大学）
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.