Prof. Li, Han Xiong,
BE-Aerospace(NUDT), IEEE Fellow, City University of Hong Kong, Hong Kong
Han-Xiong LI (李涵雄) received his B.E. degree in
aerospace engineering from the National University of Defence
Technology, China, M.E. degree in electrical engineering from
Delft University of Technology, Delft, The Netherlands, and
Ph.D. degree in electrical engineering from the University of
Auckland, Auckland, New Zealand.
Currently, he is a full professor in the Department of Systems
Engineering and Engineering Management, the City University of
Hong Kong. Over the last thirty years, he has had opportunities
to work in different fields, including military service,
investment, industry, and academia. He published over 190 SCI
journal papers with h-index 36 (ISI web of science). He has been
rated as highly cited Chinese scholar by Elsevier since 2014.
His current research interests are in system intelligence and
control, integrated process design and control, distributed
parameter systems, intelligent learning and decision
Dr. Li serves as Associate Editor of IEEE Transactions on
Systems, Man & Cybernetics: system (2016- ), IEEE Transactions
on Cybernetics (2002-2016), and IEEE Transactions on Industrial
Electronics (2009-2015). He was awarded the Distinguished Young
Scholar (overseas) by the China National Science Foundation in
2004, a Chang Jiang professor by the Ministry of Education,
China in 2006, and a national professorship in China Thousand
Talents Program in 2010. He serves as the distinguished expert
for Hunan Government and China Federation of Returned Overseas.
He is a fellow of the IEEE.
Speech Title: Smart Sensing based Intelligent Modeling for Multi-scale Dynamic Systems
Abstract: The "Made in China 2025" initiative will require full automation in all sectors, from customers to production. This will result in great challenges to manufacturing systems in all sectors. In the future of manufacturing, all devices and systems should have sensing and basic intelligence capabilities for control and adaptation. In this talk, multiscale dynamics of the modern manufacturing system will be discussed and the multi-phase integrated design methodology will be proposed. For a real-world application, sensing and modeling are always the essential steps towards the advanced control and operation. A smart sensing based integrated modeling will be presented for the jet dispensing process – a multi-time scale dynamic system in IC packaging industry. Finally, another intelligent method will be presented for effectively modelling of the space-time coupled dynamic systems, and applied to the thermal process widely existing in the industry.
Prof. Everett X. Wang, Guangdong University of Technology, China
Everett X. Wang received the BS from Peking University in 1982. In 1986 he received the MS from Institute of Theoretical Physics, Academy of Sciences of China and Ph.D. from University of Texas at Austin in microelectronics in 1993. He then joined Intel Corporation as Sr. Engineer, Staff Engineer and Sr. Staff Engineer, working on stress modeling, quantum tunneling, quantum size effect, 3D mesh generation, hydrodynamic and Monte Carlo models. In 2000 he transferred to Photonic Technology Operation in Intel as a program manager for thermal optical switch products. In 2003 he joined Design Technology Service of Intel as team leader working on hole mobility under arbitrary stress using 2D quantum transport and Monte Carlo method. In 2006, he founded a high-tech startup for developing energy efficient transportation systems. Since 2011, he has been with Guangdong University of Technology as 100-talent-plan distinguished professor. Dr. Wang authored and co-authored 54 journal and conference papers. He also holds 34 approved and pending patents. Dr. Wang's interests include receiver and system design for global navigation satellite systems, transport models for advanced electron devices, modeling and control of robotic systems as well as deep learning in medical applications.
Speech Title: Recent Progress in Robotics Bicycle for Ridesharing Applications
Abstract: Ridesharing of bicycles and electric scooters has surged in major cities all over the world, benefiting many commuters by improving efficiency, reducing congestion in road and reducing parking space. One of the main problems of the ridesharing is when riders have arrived at their destinations, they park their vehicles in their most convenient spots, often blocking traffic of the others. Riding bikes and scooters also requires substantial training time in order to be safe. With the rapid progress in robotic dynamics, control and computer vision, an intelligent self-balance, self-driving scooter can solve many problems facing ridesharing today. First, with sophisticated nonlinear dynamics, bicycle and scooter can be modelled accurately, allowing complex control scheme to be deployed for self-balancing, thus greatly improving safety. Second, using only low-cost sensors, such as ultrasonic and video camera, one can realize low speed self-driving when no rider is using the vehicle. This capability will deliver the vehicle to the needed rider from its designated park space autonomously and return to its charging station autonomously when rider has arrived at his destiny. This abstract will review the recent progress in scooter modeling and control, computer vision for navigation and obstacle avoidance as well as our currently prototype for the perfect ridesharing vehicle.
Prof. Chun-Yi Su, Concordia University, Canada
Dr. Su received his B.E. degree in control engineering from Shaanxi Institute of Mechanical Engineering (now Xi'an University of Technology) in 1982, his M.S. and Ph.D. degrees in control engineering from South China University of Technology, China, in 1987 and 1990, respectively. His Ph.D. study was jointly directed at Hong Kong Polytechnic (now The Hong Kong Polytechnic University), Hong Kong. After long stint at the University of Victoria (1991-1998, Canada), he joined the Concordia University (Canada) in 1998, where he is currently Concordia Research Chair in Control and Professor of Mechanical Engineering. He has also held several short-time visiting positions in Japan, Singapore, China and New Zealand. Dr. Su's research covers control theory and its applications to various mechanical systems. His current main research interests are in control techniques for smart material based actuators, robotic and mechatronic systems, vehicle suspension and vibration, and nonlinear control systems. He is the author or co-author of over 180 publications, which have appeared in journals, as book chapters and in conference proceedings. Dr. Su is an Associate Editor of IEEE Transactions on Control Systems Technology, IEEE Transactions on Automatic Control, and Journal of Control Theory & Applications. He is on the Editorial Advisory Board of Mechatronics and on the Editorial Board of International Journal of Intelligent Systems Technologies and Applications. He has served on the technical program committee of numerous conferences in the area of control and automation. He has served as committee chairs of a number of these conferences, including the Program Chair of IEEE CCA07.
Speech Title: Modeling and Control of Hysteresis Nonlinearities in Smart Actuators: Magnetostrictive Actuator Case
Abstract: Magnetostrictive actuators featuring high energy densities, large strokes and fast responses are playing an increasingly important role in micro/nano-positioning applications. However, such actuators with different input frequencies and mechanical loads exhibit complex dynamics and hysteretic behaviors, posing a great challenge on applications of the actuators. To this end, a comprehensive model is developed. According to the proposed hysteresis model, an inverse Asymmetric Shifted Prandtl-Ishlinskii (ASPI) Model is proposed for the purpose of compensating the hysteresis effect. However, in real systems, there always exists a modeling error between the hysteresis model and the true hysteresis. The use of an estimated hysteresis model in deriving the inverse compensator would yield some degree of hysteresis compensation error. To accommodate such a compensation error, an analytical expression of the inverse compensation error is derived first. Then, a prescribed adaptive control method is developed to suppress the compensation error and simultaneously guaranteeing global stability of the closed loop system with a prescribed transient and steady-state performance of the tracking error. The effectiveness of the proposed control scheme is validated on the magnetostrictive- actuated experimental platform.
Dr. Vojtech Vonasek, Czech Technical University (FEE-CTU), Czech
Vojtech Vonasek is a post-doc researcher at the Department of Cybernetics, Faculty of Electrical engineering, Czech Technical University (FEE-CTU) in Prague. He received his Ph.D. degree in artificial intelligence and biocybernetics from FEE-CTU in 2016. During his Ph.D. studies, he spent one year at Karlsruhe Institute of Technology (KIT), Institut für Anthropomatik und Robotik (IAR) - Intelligent Prozessautomation und Robotik (IPR) Karlsruhe, Germany. He was a post-doc researcher at the Institut für Werkzeugmaschinen und Fabrikbetrieb, Technische Universität Berlin, Berlin, Germany within German Academic Exchange Service (DAAD) post-doc programme in 2017. He serves as the reviewer for Autonomous Robots, IEEE Robotics and Automation Letters, International Journal of Automation and Computing, Robotics and Autonomous Systems, International Journal of Advanced Robotic Systems and Journal of Mechanical Engineering Science. His current research interests include path and motion planning for various robotic systems, automatic learning of locomotion gaits of modular robots and application of motion planning techniques in computational biochemistry.
Speech Title: Guided sampling of high-dimensional configuration spaces
Abstract: Path and motion planning problems can be solved by exploring the related configuration space, which is usually achieved using sampling-based planners like Rapidly Exploring Random Tree (RRT) or Probabilistic Roadmaps (PRM). The talk will give an overview of recent achievements in the area of sampling-based planning with the focus to guided-based sampling. In the guided sampling, external knowledge is used to steer the search in the high-dimensional configuration space, e.g., using features of the workspace. Basic guiding techniques for robots with few DOF (Degrees of Freedom) will be described with their extensions for motion planning of many-DOF robots like modular robots. A system for automatic learning of locomotion gaits for modular robots will be presented. The performance of the proposed methods will be demonstrated in scenarios with simulated as well as real robots. Finally, I will show an application of sampling-based planning in the area of computational biochemistry.