Keynote Addresses

Yaonan Wang

Hunan University, China

王耀南，中国工程院院士，机器人技术与智能控制专家。湖南大学教授、博士生导师、机器人视觉感知与控制技术国家工程实验室主任。2001-2020年湖南大学电气与信息工程学院院长，2016-2020年湖南大学机器人学院院长。德国洪堡学者、欧盟第五框架国际合作重大项目首席科学家， 入选国家“百千万人才工程”、中国自动化学会会士、中国计算机学会会士、中国人工智能学会会士、国家863计划智能机器人领域主题专家。兼任中国自动化学会常务理事、中国人工智能学会监事、教育部科技委能源与交通学部委员、湖南省自动化学会理事长等。成果获国家技术发明二等奖1项、国家科技进步二等奖4项、国际IEEE机器人与自动化领域“工业应用最高奖”。培养博士60余名（含IEEE Fellow、长江学者、国家杰青等），发表SCI论文170余篇，出版著作9部，获国家发明专利80余项。获得全国高等学校优秀教师、全国五一劳动奖章、全国先进工作者、 全国创新争先奖等荣誉称号。

Yaonan Wang, Academician of Chinese Academy of Engineering, the expert of robotics and intelligent control. He is Professor, doctoral supervisor, and director of the National Engineering Laboratory for Robot Visual Perception and Control at Hunan University. He was the dean of the School of Electrical and Information Engineering at Hunan University from 2001 to 2020, and the dean of the School of Robotics at Hunan University from 2016 to 2020. He was a Humboldt Fellow in Germany, the chief scientist of an international cooperation major project under EU's Fifth Framework. He is a national candidate for the “New Century Talents Project”. Prof. Wang is a fellow of Chinese Association of Automation, a fellow of the China Computer Federation, a fellow of the Chinese Association for Artificial Intelligence, and a principal expert of the National 863 Program in the field of intelligent robots. He is also an Executive Director of Chinese Association of Automation, a Supervisor of Chinese Association for Artificial Intelligence, a member of Energy and Transportation Department in Science and Technology Commission of Ministry of Education, and the Chairman of Hunan Association of Automation. Yaonan Wang won 1 Second Prize of State Technological Invention Award, 4 Second Prizes of State Scientific and Technological Progress Award, and "The Highest Award of Industrial Application" in the field of IEEE robotics and automation. Under his guidance, more than 60 PhDs (including IEEE Fellows, The Yangtze River Scholars, National Distinguished Young Scholars under NSFC) were graduated. Prof. Wang published more than 170 SCI indexed papers and 9 books, and he is the holder of more than 80 national invention patents. He was also honored as Excellent Teacher of National Colleges and Universities, National Advanced Worker, and received National May 1st Labor Medal, National Innovation Competition Award and other honorary titles.

报告题目:多机器人协作关键技术应用与发展趋势

摘要：多机器人协作是具有协同感知、规划决策、优化控制、执行功能的智能系统技术，它是信息技术和人工智能的深度融合。多机器人协作系统在国防、工业、农业等领域都具有重要的应用价值和广泛的应用前景。1.报告概述了多机器人研究背景及意义，国内外研究现状，对现有人工智能技术提出的巨大挑战，亟需研究多机器人协同感知与控制技术。2.详细介绍了多机器人协作的技术难题及解决方案。3.探讨了协同视觉感知、高效规划、多机协同控制等关键技术，并应于智能制造、国防等领域。4.总结与展望多机器人发展与前景。

Title: Applications and Development Trend of Key Technologies for Multi-robots Collaboration

Abstract: Multi-robots collaboration is an intelligent system technology, which has the function of collaborative perception, planning and decision, optimization and control, and execution. It is a deep integration of information technology and artificial intelligence. It has important value and broad prospect for application in the fields of national defense, industry, and agriculture. 1. In this report, the background and significance and state of art of multi-robot research is overviewed. In face of the challenges to the existing artificial intelligence technology, there is an urgent need for studying multi-robots collaborative sensing and control technology. 2. The technical problems and solutions of multi-robot collaboration are introduced in detail. 3. The key technologies such as collaborative visual perception, high-efficiency planning and multi-agent collaborative control are discussed and applied to intelligent manufacturing, national defense and other fields. 4. A summary and outlook on the development and prospects of multi-robots is given.

Speaker

Bin Jiang

Nanjing University of Aeronautics and Astronautics, China

姜斌，南京航空航天大学教授、博导、副校长，IEEE Fellow，教育部“长江学者”特聘教授， 中国自动化学会会士。曾经先后在新加坡、法国、美国、加拿大做博士后、研究员、邀请教授和访问教授。目前担任国际期刊 IEEE Trans. on Cybernetics， Neurocomputing，J. of Franklin Institute, 和国内期刊《宇航学报》、《控制与决策》、《系统工程与电子技术》等多个学术期刊的编委、Int. J. Control, Automation and Systems 领域主编，《控制工程》副主编，IEEE南京分部控制系统分会主席，中国航空学会导航、制导与控制分会副主任，中国自动化学会技术过程故障诊断与安全性专业委员会副主任， 中国自动化学会数据驱动控制与学习系统专委会委员，江苏省自动化学会副理事长。从事故障诊断和容错控制及其在飞控系统和高铁牵引系统中的应用研究，主持获得国家自然科学二等奖、教育部自然科学一等奖、江苏省科技一等奖等科研奖励；获得授权发明专利28项，出版学术专著8部，在IEEE Transactions, Automatica, AIAA JGCD，中国科学，自动化学报等国内外学术期刊发表论文80余篇。

Jiang Bin is professor, doctoral supervisor and vice president of Nanjing University of Aeronautics and Astronautics, IEEE fellow, distinguished professor of Cheung Kong Scholar Program in the Ministry of Education, and member of Chinese Association of Automation. He has been postdoctoral, researcher, invited professor and visiting professor in Singapore, France, the United States and Canada. At present, he is member of editorial board of several academic journals. There are international academic journals such as IEEE Trans. on Cybernetics, Neurocomputing, J. of Franklin Institute, and domestic academic journals such as Journal of Astronautics, Control and Decision, System Engineering and Electronic Technology. He has been chief editor of Int. J. Control, Automation and Systems, deputy editor of Control Engineering, chairman of control system branch of Nanjing branch of IEEE, deputy director of Guidance, Navigation and Control branch of CSAA, deputy director of Technical Process Fault Diagnosis and Safety Professional Committee of CAA, member of Data Driven Control and Learning System Special Committee of CAA, vice president of Jiangsu Association of Automation. He works for research on application of fault diagnosis and fault-tolerant control in flight control system and high-speed railway traction system. He has won the second prize of National Natural Science Award, the first prize of Natural Science Award of Ministry of Education, the first prize of Science and Technology of Jiangsu Province and other scientific research awards. He has obtained 28 authorized invention patents and published 8 academic monographs. More than 80 papers have been published in IEEE Transactions, Automatica, AIAA JGCD, Science China, Acta Automatica Sinical and other academic journals.

报告题目： 高速列车牵引传动系统故障诊断、预测与容错控制技术

摘要： 作为高效便捷运输工具之一高速列车，随着其全世界的普及，其安全性和可靠性也越来越受到重视。牵引传动系统为高速列车提供动力，其包含整流器，逆变器，牵引电机，中间电容等电气设备，一旦发生故障会导致列车损失动力，造成减速、停车甚至事故。因此，开展基于模型和数据驱动的牵引系统故障诊断、剩余寿命预测与容错控制研究具有重要的意义。针对高速列车牵引系统和设备级故障进行建模和传播机理分析，考虑到列车运行中的干扰和噪声，研究干扰下故障诊断、预测与容错控制方法，基于半物理仿真实验平台和车载实验开展了牵引传动系统的故障诊断应用研究。

Title: Fault Diagnosis, Prediction and Fault-tolerant Control Technology for Traction Drive System of High-speed train

Abstract: As one of the efficient and convenient means of transportation, with its popularity all over the world, safety and reliability of high-speed train have been paid more and more attention. Traction drive system, which includes rectifier, inverter, traction motor, intermediate capacitor and other electrical equipment, provides power for high-speed train. In case of failure, the train will lose power, resulting in deceleration, parking and even accidents. Therefore, it is of great significance to carry out the research on fault diagnosis, prediction of residual life and fault-tolerant control of traction system based on model and data-driven. In this paper, the modeling and propagation mechanism analysis of high-speed train traction system and equipment-level faults are carried out. Considering the interference and noise in train operation, the fault diagnosis, prediction and fault-tolerant control method under interference are studied. Based on semi physical simulation experimental platform and on-board experiment, research on application of fault diagnosis on traction drive system is carried out.

Speaker

Zongli Lin

University of Virginia, USA

Zongli Lin is the Ferman W. Perry Professor in the School of Engineering and Applied Science and a Professor of Electrical and Computer Engineering at the University of Virginia. He received his B.S. degree in Mathematics and Computer Science from Xiamen University, Xiamen, China, in 1983, his Master of Engineering degree in automatic control from Chinese Academy of Space Technology, Beijing, China, in 1989, and his Ph.D. degree in electrical and computer engineering from Washington State University, Pullman, Washington, in 1994. His current research interests include nonlinear control, robust control, and control applications. Professor Lin has served on the editorial boards of several journals, including those of IEEE Transactions on Automatic Control, IEEE/ASME Transactions on Mechatronics, IEEE Control Systems Magazine, and IEEE/CAA Journal Automatica Sinica. He was elected a member of the Board of Governors of the IEEE Control Systems Society (2008-2010 and 2019-2021) and chaired the IEEE Control Systems Society Technical Committee on Nonlinear Systems and Control (2013-2015). He has also served on the operating committees of several conferences and was the program chair of the 2018 American Control Conference and a general chair of the 13th and 16th International Symposia on Magnetic Bearings, held in 2012 and 2018, respectively. He currently serves on the editorial boards of several journals and book series, including Automatica, Systems & Control Letters, Science China Information Sciences, and Springer/Birkhauser book series Control Engineering. He is a Fellow of IEEE, IFAC, AAAS and CAA.

Title: Control of linear systems subject to actuator saturation: from model-based design to reinforcement learning control

Abstract: This talk will discuss the problem of controlling a linear system subject to actuator saturation through reinforcement learning. In particular, it is illustrated how the model-based control design techniques motivate the design of iterative Q-learning algorithms for global asymptotic stabilization of discrete-time linear systems that are asymptotically null controllable with bounded control. It is hoped that the discussion will stimulate interest in constrained control problems among the data driven control and learning systems research community.

Speaker

Thomas Parisini

Imperial College London & University of Trieste，UK

Prof. Thomas Parisini received the Ph.D. degree in electronic engineering and computer science from the University of Genoa, Genoa, Italy, in 1993. He was with Politecnico di Milano and since 2010, he has been holding the Chair of Industrial Control and is the Director of Research with Imperial College London, London, U.K. He is a Deputy Director of the KIOS Research and Innovation Centre of Excellence, University of Cyprus, Nicosia, Cyprus. Since 2001, he has also been the Danieli Endowed Chair of Automation Engineering with University of Trieste, Trieste, Italy. In 2009–2012, he was the Deputy Rector of University of Trieste. In 2018, he received an Honorary Doctorate from University of Aalborg, Aalborg, Denmark. He authored or coauthored more than 320 research papers in archival journals, book chapters, and international conference proceedings. His research interests include neural-network approximations for optimal control problems, distributed methods for cyber-attack detection and cyber-secure control of large-scale systems, fault diagnosis for nonlinear and distributed systems, nonlinear model predictive control systems, and nonlinear estimation.

Dr. Parisini was the Co-recipient of the IFAC Best Application Paper Prize of the Journal of Process Control, Elsevier, for the three-year period 2011–2013 and of the 2004 Outstanding Paper Award of the IEEE Transactions on Neural Networks. He was also the Recipient of the 2007 IEEE Distinguished Member Award. In 2016, he was awarded as Principal Investigator at Imperial of the H2020 European Union flagship Teaming Project KIOS Research and Innovation Centre of Excellence led by University of Cyprus with an overall budget of over 40 Million Euro. In 2012, he was awarded an ABB Research Grant dealing with energy-autonomous sensor networks for self-monitoring industrial environments.

He is currently the 2020 President-Elect of the IEEE Control Systems Society and will serve as thre 2021-2022 President. He has served as Vice-President for Publications Activities and during 2009–2016, he was the Editor-in-Chief for the IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY. Since 2017, he has been the Editor for Control Applications of Automatica and since 2018 he has been the Editor-in-Chief for the European Journal of Control. He served as Chair of the IFAC Technical Committee on Fault Detection, Supervision and Safety of Technical Processes–SAFEPROCESS. He was the Chair of the IEEE Control Systems Society Conference Editorial Board and a Distinguished Lecturer of the IEEE Control Systems Society. He was an elected member of the Board of Governors of the IEEE Control Systems Society and of the European Control Association and a Member of the board of evaluators of the 7th Framework ICT Research Program of the European Union and member the ERC Advanced and Consolidator Grant board of Reviewers.He is currently an Associate Editor for the International Journal of Control and was an Associate Editor for the IEEE TRANSACTIONS ON AUTOMATIC CONTROL, IEEE TRANSACTIONS ON NEURAL NETWORKS, Automatica, and International Journal of Robust and Nonlinear Control.

Among other activities, he was the Program Chair of the 2008 IEEE Conference on Decision and Control and General Co-Chair of the 2013 IEEE Conference on Decision and Control. He is a Fellow of the IEEE and the IFAC.

Title: Digital Twins for Distributed Fault Detection in the Process Industry

Abstract: In an increasingly "smarter" planet, it is expected that interconnected process systems will be safe, reliable, available 24/7, and of low-cost maintenance – the Industry 4.0 vision. Therefore, health monitoring, fault diagnosis and fault-tolerant control are of customary importance to ensure high levels of safety, performance, reliability, dependability, and availability. In the lecture, the process industry I considered as a paradigmatic context in which, faults and malfunctions can result in off-specification production, increased operating costs, production line shutdown, danger conditions for humans, detrimental environmental impact, and so on. Faults, malfunctions and cyber-attacks need to be detected promptly and their source and severity should be diagnosed so that corrective actions can be taken as soon as possible. Once a fault is detected, the faulty subsystem can be unplugged to avoid the propagation of the fault in the interconnected large-scale system. Analogously, once the issue has been solved, the disconnected subsystem can be re-plugged-in.

High-fidelity digital twins represent a game-changing key enabling technology to design effective and accurate distributed fault diagnosis systems in the absence of reliable process data under faulty scenarios. A real industrial use-case is addressed in the lecture.