Invited Speakers | 特邀专家

Prof. Ning Sun
Nankai University, China

Ning Sun received the B.S. degree in measurement & control technology and instruments (with honors) from Wuhan University, Wuhan, China, in 2009, and the Ph.D. degree in control theory and control engineering (with honors) from Nankai University, Tianjin, China, in 2014. He is currently an IEEE Senior Member.

He is currently a Full Professor with the Institute of Robotics and Automatic Information Systems, Nankai University, Tianjin, China. He was awarded the prestigious Japan Society for the Promotion of Science (JSPS) Postdoctoral Fellowship for Research in Japan (Standard). His research interests include intelligent control for mechatronic/robotic systems with emphasis on (industrial) applications.

Dr. Sun received the Wu Wenjun Artificial Intelligence Excellent Youth Award in 2019, the China 10 Scientific and Technological Developments in Intelligent Manufacturing (2nd achiever) in 2019, the First Class Prize of Wu Wenjun Artificial Intelligence Natural Science Award in 2017, the First Class Prize of Tianjin Natural Science Award in 2018, the Golden Patent Award of Tianjin in 2017, the IJCAS (International Journal of Control, Automation, and Systems) Academic Activity Award in 2018 and 2019, the Outstanding Ph.D. Dissertation Award from the Chinese Association of Automation (CAA) in 2016, etc.

He is the Executive Editor for Measurement and Control and serves as an Associate Editor (editorial board member) for several journals, including IEEE ACCESS, Frontiers in Neurorobotics, International Journal of Control, Automation, and Systems, IET Cyber-Systems & Robotics, Transactions of the Institute of Measurement and Control, International Journal of Precision Engineering and Manufacturing, etc. Dr. Sun has been an Associate Editor of the IEEE Control Systems Society (CSS) Conference Editorial Board (including ACC, IEEE CDC) since July 2019, and he is an Associate Editor for IEEE ICRA 2021 and IEEE/RSJ IROS 2020.

Speech Title: Modeling, Analysis, and Intelligent Control of Pneumatic Artificial Muscle-Actuated Robots
Abstract: With the rapid development of rehabilitation robots and the growing demands for human-robot interaction, modeling and intelligent control of pneumatic artificial muscle (PAM) robots have increasingly attracted the attention of many researchers. It is a challenging research topic to overcome the effects of PAMs’ inherent defects (e.g., high nonlinearities, hysteresis, time-varying characteristics, etc.), despite the merits of lightness, safety, and high power-to-weight/volume ratios of PAMs. To this end, we aim to achieve accurate modeling and advanced control for PAM robots, which may contribute to their further theoretical research and practical applications. Specifically, for single-PAM robots, there exist some difficulties as follows: 1) PAM systems are susceptible to unknown external disturbances due to their high nonlinearities, creep, hysteresis, etc. 2) PAM robots usually suffer from parameter uncertainties and unmodeled dynamics. 3) The ultimate control inputs (corresponding to the pressurized air) of PAM robots should be constrained to be nonnegative. To solve these problems, we propose a disturbance estimation-based nonlinear control method, a neuroadaptive control method with system uncertainties, and an adaptive control method with unidirectional input constraints, respectively. Further, for multi-PAM robots, the following issues should be considered: 1) Since torques/forces are generated by air pressure and are not the ultimate control inputs, the torque models of PAM robots are not direct and effective. 2) To ensure safety, the system state variables (e.g., contracted lengths of muscles, ranges of robots’ movements, etc.) are usually limited. To this end, we propose an accurate dynamic modeling method and a nonlinear control method with overshoot constraints, respectively. Some future research directions will also be discussed.


Prof. Mara Tanelli
Politecnico di Milano, Italy

Mara Tanelli was born in Lodi, Italy, in 1978. Since October 2020, she is Full Professor of Automatic Control at the Dipartimento di Elettronica e Informazione of the Politecnico di Milano, where she obtained the Laurea degree in Computer Science Engineering in 2003 and the Ph.D. in Information Engineering in 2007, summa cum laude. She also holds a M.Sc. in Computer Science from the University of Illinois at Chicago. For her Ph.D. research activities, in 2007 she has been awarded with the Dimitri N. Chorafas Ph.D. Thesis Award and the Claudio Maffezzoni Ph.D. Thesis Award, second edition.

Her main research interests focus on active control systems design for ground vehicles, estimation and identification techniques with application to automotive systems, energy management of electric vehicles, smart mobility, insurance telematics and sliding mode control. She is author and co-author of more than 150 peer-reviewed scientific publications and 15 patents in these research areas. For her research activities, she has been awarded with the 2008 ASME Dynamic Systems and Control Rudolf Kalman Best Paper Award. In 2011, she received the Control Engineering Practice Best Paper Prize. In 2014, she obtained the IEEE–CSS Italian Chapter Best Young Author Journal Paper Award 2013. Since 2012, she is Senior Member of the IEEE and since 2013 she is a member of the Conference Editorial Board of the IEEE Control Systems Society. She is currently Associate Editor of the IEEE Transactions on Control Systems Technology and of the IEEE Transactions on Human-Machine Systems.

Speech Title: Shaping the Adoption of Electric Vehicles: A Data-based Approach

Abstract: In the next few years, crucial transitions will occur within the realm of sustainability, with mobility having a major share. In view of sustainability needs, novel mobility models need to emerge: they must be smart enough to answer the multifaceted needs of their users, and, of course, environmentally friendly and energy efficient. Electric Vehicles (EVs) are crucial to support the shift towards green mobility models, and governments all around the globe are actively designing policies to support EV mass adoption. In this talk, I will present a network-based adoption model, whose multi-class agents are potential EV users modeled based on data describing their driving habits, measured on instrumented vehicles. Starting from the interaction network, built based on physical proximity links among users, a cascade model is used to investigate the dynamics of the open-loop adoption mechanism. Then, a policy-design framework is proposed and its cost/benefit effects quantified and discussed.


Prof. Xiongbiao Luo
Xiamen University, China

Xiongbiao Luo received his PhD degree in Information Science from Nagoya University, Japan, in 2011. He was a Postdoctoral Fellow and Assistant Professor in Nagoya University, Japan, Visiting Assistant Professor at Technical University of Munich, Germany, Postdoctoral Fellow in the University of Western Ontario, Canada, and Researcher in the French National Institute of Health and Medical Research, France, and Principal Technical Consultant of Boston Scientific Corporation, USA. Since 2015, he is a Full Professor with the Department of Computer Science at the School of Informatics, Department of Basic Medical Sciences at the School of Medicine, and Director of XMU Center for Surgery and Engineering, Xiamen University, China.

His current interest includes artificial intelligence in healthcare, medical imaging, computer vision, computational photography, surgical tracking and navigation, and medical robotics. He edited six books at Springer and has more than 120 peer-reviewed publications on these subjects in flagship journals and conferences including IEEE Transactions on Medical Imaging, Medical Image Analysis, Annual Review of Biomedical Engineering, and IEEE Transactions on Biomedical Engineering, MICCAI and CVPR.

He is an Associate Editor of IEEE Robotics and Automation Letters, and Associate Editor of IEEE Transaction on Medical Robotics and Bionics, Area Chair (2017-) of International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), Program Committee Member (2019-) of International Congress and Exhibition on Computer Assisted Radiology and Surgery (CARS). He is a Senior Member of IEEE and also serves as a Reviewer for more than 37 international journals and conferences.

Speech Title: Augmented da Vinci Robotic Imaging

Abstract: Successful da Vinci robotic assisted surgical procedures depend significantly on the visualization quality of in situ endoscopic imaging. The visibility and maintenance of such direct in situ visualization is paramount not only for safety by preventing inadvertent injury but also to improve precision and reduce operating time. Unfortunately, the robotic surgical field is problematic. This speech will discuss several common problems in da Vinci robotic imaging during surgery, e.g., illumination nonuniformity, smoke, recognition or identification of anatomical structures of interest, and 3D reconstruction, as well as updates current achievements from Laboratory for Intelligent Medical Vision, Navigation, and Robotics, Xiamen University, China.


Prof. Zhendong Sun
Chinese Academy of Sciences, China

Zhendong Sun is with the Key Laboratory of Systems & Control, Academy of Mathematics & Systems Science, Chinese Academy of Sciences, where he is currently a Researcher. His research interests are in the fields of nonlinear control systems, switched and hybrid systems, and nano-micro-electronic systems. He is the first author of the monographs ``Switched Linear Systems-Control and Design'' and ``Stability Theory of Switched Dynamical Systems (London: Springer, 2005 & 2011). He serves/served as Associate Editor for IEEE Transactions on Automatic Control and International Journal of Robust and Nonlinear Control.

Speech Title: Switched Linear Control Systems--Decomposition, Canonical Forms, and Feedback Stabilization

Abstract: In this talk, I focus mainly on the stabilization problem of switched linear control systems, and present a constructive design scheme that extends the standard system decomposition approach for linear systems. The design steps are re-reviewed, and the challenges are discussed. Latest progress will be briefly introduced.


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