Prof. Wei He

University of Science and Technology Beijing, China

Speech Title: Intelligent Control Design of Collaborative Robots
Abstract: Collaborative robots have the advantages of flexible deployment and simple operation, which are widely used in advanced manufacturing, social service, etc. Autonomous manipulation and human robot collaboration for cobots consist of several challenges: cobot dynamic uncertainties, complex dynamic environment, multiple constraints, and so on. Intelligent control design provides an enabling technology for developing cobots, which becomes one of the research focuses. This report aims at discussing intelligent control design and integrated systems by addressing the challenges discussed above. Firstly, this talk focuses operation accuracy of cobots. High precision intelligent control design considering uncertainties and multiple constraints are introduced. Secondly, addressing safe physical interaction between cobots and the complex environment, it expounds the impedance control design involving human motion intention estimation and impedance learning. Then, this talk introduces the proposed intelligent control design of cobots as applied to self-integrated intelligent sorting systems and human-robot collaborative systems. Finally, the related research fields are presented.

Biography: Professor Wei He received his B.Eng. and his M.Eng. degrees from College of Automation Science and Engineering, South China University of Technology (SCUT), China, in 2006 and 2008, respectively, and his PhD degree from Department of Electrical Computer Engineering, the National University of Singapore (NUS), Singapore, in 2011. He is currently working as a full professor in Institute of Artificial Intelligence and School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing, China. He has co-authored 3 books published in Springer and published over 100 international journal and papers. He is serving as an Associate Editor of IIEEE Transactions on Neural Networks and Learning Systems, IEEE Transactions on Control Systems Technology, SCIENCE CHINA Information Sciences, IEEE/CAA Journal of Automatica Sinica. His current research interests include robotics, distributed parameter systems and intelligent control systems.


Prof. Hongyi Li

Guangdong University of Technology, China

Speech Title: Human-in-the-Loop Cooperative Control for Multi-Agent Systems
Abstract: In recent years, cooperative control of multi-agent systems has become a research hot in the artificial intelligence field, which mainly includes consensus control, containment control, formation control etc., and is widely used in aerospace systems, electric power systems, multi-robot systems, multi-UAV systems, and so on. However, the artificial intelligence technology is not mature, which makes the fully autonomous machine nonexistent. Moreover, traditional autonomous control systems often ignore the human intervention, once the unknown and complex working condition appears, it is easy to occur decision risk or make the system out of control, resulting in accidents. Therefore, it is necessary to study the human-in-the-loop cooperative control problem of multi-agent systems. By using the physical equipment, the operator participates in the cooperative control to obtain the environment and system information, and further makes the decision for the next step action via thinking and analyzing. Since the operator can indirectly control followers by directly controlling the leader, the control task is completed better and the system reliability and safety is improved. This report is divided into three parts: First, the research background and several application examples of human-in-the-loop control are reported. Then, some works with regard to the human-in-the-loop cooperative control of multi-agent systems is introduced. Finally, the main work is summarized and future researches are also given.

Biography: 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.


Prof. Tao Liu

Dalian University of Technology, China

Speech Title: In-situ Process Measurement by Infrared Spectra and Microscopic Image Analysis and Data-driven Control & Optimization
Abstract:Infrared spectra and microscopic image technologies have been increasingly applied for in-situ measurement and data-driven control & optimization of industrial manufacturing processes in the past twenty decades. This talk will present a series of in-situ process measurement methods based on infrared spectra and microscopic image analysis explored by our research group. Moreover, by using real-time measurement and historical batch run data, data-driven control & optimization methods are presented with application to industrial crystallization processes. Finally, an outlook on these prospective approaches is given to draw attentions for exploration in the future.

Biography: Tao Liu received the PhD degree in Control Science and Engineering from Shanghai Jiaotong University, Shanghai, China, in 2006. He had been a postdoctoral research fellow and later a research assistant professor in the Department of Chemical and Biomolecular Engineering at Hong Kong University of Science and Technology from May 2006 to April 2010, and an Alexander von Humboldt research fellow in the Institute of Process Systems Engineering at RWTH Aachen University in Germany from May 2010 to June 2012. He is a professor and head of the Institute of Advanced Control Technology at Dalian University of Technology. His research interests include in-situ measurement of industrial and chemical processes, data-driven process modelling and state estimation, robust process control, batch process optimization. He published more than 100 research papers and two monographs. He serves as an associate editor of ISA Transactions, Systems Science and Control Engineering, an editorial board member of International Journal of Control, a member of the Technical Committee on Chemical Process Control of IFAC, Technical Committee on System Identification and Adaptive Control of the IEEE Control System Society, Chinese Control Theory Committee and Process Control Committee.


Prof. Zhigang Liu

Southwest Jiaotong University, China

Speech Title: Modeling and Suppression of Low-frequency Oscillation between Locomotives and Traction Networks in Electrification Railways
Abstract:
Recently, the low-frequency oscillation issues between locomotives and traction networks in high-speed railways is one of the research hot points in the electrical protection of high-speed railways. How to completely reveal the mechanism of low-frequency oscillation issues and effectively suppress them are the two key problems. This presentation will focus on the key issues such as small-signal impedance modelling, low-frequency oscillation mechanism, and instability suppression strategy of locomotives converter and traction network. First, the dq model, mirror-frequency impedance model, and harmonic state-space model are introduced for the single-phase power electronic converter system. A unified modelling framework in the dq-frame, the mirror-frequency frame, and the extended harmonic domain frame is created, and an unambiguous relationship between the impedances in these three domains is presented. Second, some advanced control strategies for suppressing the low-frequency oscillation are discussed in detail in the vehicle, such as H∞ control, sliding mode control, passivity-based control, etc. In the end, dynamic compensators, such as active power filter (APF) and static synchronous compensator (STATCOM), are used to mitigate the low-frequency oscillation. An idea based on the dq-frame admittance decomposition is proposed to investigate the impact of APF and STATCOM on the system stability. The coupling admittance of these devices plays a significant role in the load admittance reshaping and determining their ability to enhance the system stability and suppress the low-frequency oscillation issues.

Biography: Zhigang Liu (IET Fellow, Senior Member IEEE) received a Ph.D. degree in Power systems and its Automation from Southwest Jiaotong University, China in 2003. He is a Full Professor at the School of Electrical Engineering since 2006, Southwest Jiaotong University, Chengdu. He is also a Guest Professor at Tongji University, Shanghai. Dr. Liu is an Associate Editor-in-Chief of IEEE TIM, Associate Editor of IEEE TNNLS, IEEE TVT, and IEEE ACCESS. He received the IEEE TIM's Outstanding Associate Editors for 2019, 2020, and 2021. Dr. Liu is an Editorial board member of journals such as "ACTA AUTOMATICA SINICA", "Journal of Southwest Jiaotong University", etc. He won Second Prize in the National Science and Technology Progress Award, the Sichuan Youth Science and Technology Award, the Special Prize of the China Railway Society, Second Prize of the Science and Technology Progress Award of the Ministry of Education, First Prize of the Sichuan Higher Education Teaching Achievement, National Hundred Excellent Doctoral Dissertation Nominations, China Electric Power Excellent Technologist Award, Fok Yingdong Young, Fund Mao Yisheng Railway Science, and Technology Award, and the Zhan Tianyou Special Fund Award.



Prof. Zehui Mao

Nanjing University of Aeronautics and Astronautics

Speech Title: Adaptive Control/Fault-Tolerant Control for High-Speed Trains
Abstract: High-speed trains with their high loading capacities, fast and on schedule, have been one of the most important transportation means. In studies of train control, there are mainly two types of models used in the literatures, namely, the single mass point model and the cascade mass point model. For the commonly used single mass point model, a new adaptive fault-tolerant sliding-mode control scheme is proposed, in which the external disturbance, actuator faults and actuator uncertainty modelled as input distribution matrix uncertainty are considered. For the multibody high-speed train dynamic model in the presence of unknown parameters, an adaptive controller is designed and its evaluation is developed, which can achieve the desired closed-loop system signal boundedness and asymptotic speed tracking.

Biography: Zehui Mao received the Ph.D. degree in Control Theory and Control Engineering at Nanjing University of Aeronautics and Astronautics, Nanjing, China, in 2009. She had ever been a Post-Doctoral Research Fellow and Visiting Scholar in Tsinghua University, Beijing, China and the University of Virginia, Virginia, USA, respectively. She is currently a Professor in College of Automation Engineering at Nanjing University of Aeronautics and Astronautics.
She has been the principle investigator on several projects of National Natural Science Foundation of China. She is the author of over 70 referred international journal papers and conference papers. She won Second Class Prize of National Natural Science Award of China in 2018 (No.4). Her research interests include fault diagnosis and fault-tolerant control and its application in flight control systems and high-speed rail traction systems.


Prof. Chaoxu Mu

Tianjin University, China

Speech Title: Reinforcement Learning for the cooperative problem of unmanned systems
Abstract: The rapid development of reinforcement learning and deep learning methods makes it possible for variable unmanned systems to improve efficiently their performance in challenging environments. This report will introduce the principles of reinforcement learning and deep reinforcement learning methods, analyze the key problems of multiple unmanned systems in cooperative control, and provide some ideas for unmanned systems cooperation with reinforcement learning.

Biography: Dr. Chaoxu Mu is a Professor with the School of Electrical and Information Engineering, Tianjin University, China. She has published over 80 peer-reviewed journal and conference papers, and jointly published 2 books with Springer and Chinese Science Press. All papers have been cited more than 3500 times. Since 2009 to date, she has hosted and participated over 10 research grants from National Science Foundation of China, the Ministry of Science and Technology of China, key laboratories, and so on. She has advised 20+ Ph. D students and master students, and many undergraduate students. She is the associate editors of ACTA AUTOMATICA SINICA and IEEE TNNLS, is also invited to peer-review for multiple journals. Dr. Mu regularly serves on the committees of IEEE, China Automation Society, Chinese Association for Artificial intelligence, and various international conferences.


Prof. Yongping Pan

Sun Yat-sen University, China

Speech Title: Composite Learning Tracking and Interaction Control for Compliant Robots
Abstract: Due to the rapid population aging globally, the current trend of robotic research has been shifting from traditional industrial robots that are separated from humans to human-centered robots that coexist, cooperate, and collaborate with humans. A major motivation for introducing compliance to human-centered robots is physical human-robot interaction. This talk considers compliant robots with flexible joints and highlights three major results in composite learning control for compliant robots. First, we establish the connection of the human motor learning and control mechanism to adaptive and learning control theory. Second, we propose a composite learning technique to achieve efficient learning from the bioinspired adaptive robot control. Third, we apply composite learning control methods to improve the accuracy, safety, and naturalness of compliant robots. Experiments based on several physical robots are provided to verify the proposed methods.

Biography: Dr. Yongping Pan is a Professor who leads the Robot Control and Learning Group at the Sun Yat-sen University, China. He holds the Ph.D. degree in control theory and control engineering from the South China University of Technology, China, and has long-term research experience in top universities worldwide. His research interests include automatic control and machine learning with applications to robotics, such as compliant actuation, interaction control, visual servoing, motion planning, and dexterous manipulation. Based on his research, Dr. Pan has authored or co-authored more than 130 peer-reviewed academic papers, including over 100 papers in refereed journals, where his publications have attracted over 5300 citations on the Google Scholar. He has been invited as an Associate Editor of several top-tier journals, such as IEEE-TCSE, IEEE-TASE, and IEEE-CAL, and as a speaker to deliver academic talks in top universities and conferences over 30 times worldwide. Dr. Pan has been recognized as a World’s Top 2% Scientist by Stanford University, a Global Highly Cited Researcher by Clarivate, and a Highly Cited Chinese Researcher by Elsevier.


Prof. Zhuo Wang

Beihang University, China

Speech Title: Reachability Analysis of the Atomic Spin Polarization State for Spin-Exchange Relaxation-Free Atomic Magnetometers
Abstract:
This research is the first work to innovatively analyze and determine the reachability of the atomic spin polarization state of Spin-Exchange Relaxation-Free Atomic Magnetometers (SERFAMs), whose dynamic behaviors are described by the Bloch equations. Their Bloch equation-model is transformed into a cybernetic model to analyze the reachability. The analysis results show that the degree of freedom of the polarization state transition is not consistent with the input dimension. Although this is contrary to the physical intuition, it corresponds to the point of cybernetics. The simulation results verify this conclusion. This work is an interesting case of applying cybernetics in the study of physics, which broadens the way of thinking for the performance analysis of atomic spin polarization systems.

Biography: Zhuo Wang received the B.E. degree in Automation from Beihang University, Beijing, China, in 2006; and the Ph.D. degree in Electrical and Computer Engineering from University of Illinois at Chicago, Chicago, IL, USA, in 2013. He was a Postdoctoral Fellow with the Department of Electrical and Computer Engineering, University of Alberta, from 2013 to 2014; and then worked as a Research Assistant Professor with the Fok Ying Tung Graduate School, Hong Kong University of Science and Technology, from 2014 to 2015. Zhuo Wang was selected for the “12th Recruitment Program for Young Professionals” by the Organization Department of the CPC Central Committee, and the “100 Talents Program” by Beihang University, in 2015. As the Second Recipient, he won the First Prize of Natural Science in the Award for Outstanding Achievements in Scientific Research of Colleges and Universities (Science and Technology) by Ministry of Education of the People's Republic of China, in 2019. Then, he won the 7th Chinese Association of Automation Young Scientist Award, in 2021. Zhuo Wang joined Beihang University since January 2016, and he is currently a Professor and a Ph.D. Instructor with the Research Institute for Frontier Science, and with the Key Laboratory of Ministry of Industry and Information Technology on Quantum Sensing Technology, and with Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, Beihang University, Beijing, China; and also with Beihang Hangzhou Innovation Institute Yuhang, Hangzhou, China. Prof. Wang is currently a Vice Director of the 9th Chinese Association of Automation (CAA) Youth Work Committee; a Member of the Adaptive Dynamic Programming and Reinforcement Learning Technical Committee of IEEE Computational Intelligence Society; and is also a Member of the Data Driven Control, Learning & Optimization Professional Committee of CAA. Besides, he is an Associate Editor of IEEE Transactions on Systems, Man, and Cybernetics: Systems; an Associate Editor of Control Theory & Applications; an Associate Editor of Acta Automatica Sinica; and is also an Associate Editor of Pattern Recognition and Artificial Intelligence. Prof. Wang's research interests include data-based system identification, modeling, analysis, learning and control; nonlinear system adaptive control; atomic spin ensembles system control; as well as state estimation and signal processing of complex high precision measurement instruments.


Prof. Bin Xu

Northwestern Polytechnical University, China

Speech Title: Robust Intelligent Control of Hypersonic Flight Vehicle
Abstract: With the capability of high speed flying, a more reliable and cost efficient way to access space is provided by hypersonic flight vehicles. Controller design, as key technology to make hypersonic flight feasible and efficient, has numerous challenges. This talk addresses the control of hypersonic flight dynamics on the basis of intelligent learning and robust design. Firstly, the intelligent control with disturbance observer will be provided to deal with the wind effect and the parametric uncertainty. Secondly, in case of constraint on angle of attack (AOA), the Barrier Lyapunov Function based robust design is designed to make sure of the predefined boundaries. Thirdly, considering the elastic modes, the flexible dynamics is transformed using the singular perturbation decomposition (SPD) theory and the robust intelligent control is studied. The simulation tests are provided to show the effectiveness of the proposed approaches.

Biography: Dr Bin Xu is currently Professor with School of Automation, Northwestern Polytechnical University. He received the B.S. degree in measurement and control from Northwestern Polytechnical University, China, 2006 and the Ph.D. degree in Computer Science from Tsinghua University, China, 2012. He visited ETH Zurich from Mar 2010 to Mar 2011 and from Feb 2012 to Jan 2013 he was Research Fellow with Nanyang Technological University. From Jul 2012 to now, he has been with School of Automation, Northwestern Polytechnical University where now he is Professor. His research interests include computation intelligence, intelligent control, and adaptive control with application on flight dynamics. He serves as Editorial Board Member/Associate Editor of several international journals (including Journal of Intelligent & Robotic Systems, International Journal of Control, Automation, and Systems). He has been listed as Clarivate Highly Cited Researchers since 2019.


Prof. Lixian Zhang

Harbin Institute of Technology, China

Speech Title: Planning and Control for a Class of Rotor-Driven Hybrid Aerial and Terrestrial Vehicles
Abstract: The hybrid aerial and terrestrial (HAT) vehicles possess mobility in the air and on the ground. Compared with UAVs, UGVs, and UAV-UGV collaborations, the HAT vehicles have shown significant advantages in accessibility and endurance, leading to great potential in large-range unknown environment exploration, long-distance delivery, etc. This talk will summarize the state-of-the-art of the rotor-driven HAT vehicles, present some progresses on configuration design, planning and smooth-transition control of two classes of rotor-driven HAT vehicles via multimodal design, NMPC, etc., and finally give the future works.

Biography: Lixian Zhang received the Ph.D. degree in control science and engineering from Harbin Institute of Technology, Harbin, China, in 2006. From January 2007 to September 2008, he was a Postdoctoral Fellow in the Department Mechanical Engineering at the Ecole Polytechnique de Montreal, Canada. He was a Visiting Professor at the Process Systems Engineering Laboratory, Massachusetts Institute of Technology (MIT), Cambridge, MA, USA, from February 2012 to March 2013. Since January 2009, he has been with Harbin Institute of Technology, where he is currently full professor and vice dean of School of Astronautics. His research interests include nondeterministic switched systems, model predictive control and their applications.
Prof. Zhang serves as Senior Editor for IEEE Control Systems Letters, and served as Associate Editor for IEEE Transactions on Automatic Control, IEEE Transactions on Cybernetics, etc. He was named to the list of Clarivate Highly Cited Researchers in 2014–2021. He is a Fellow of IEEE.

 

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