Prof. Han Xiong Li, IEEE Fellow, City University of Hong Kong, China

Speech Title: Intelligence based Spatiotemporal Identification of Complex DPS for Analysis and Prediction

Abstract: We live in a world of time and space. There are many dynamic processes of space-time coupling in various fields of industry, from temperature distribution, fluid injection, to flexible mechanical arm motion. Such processes are collectively referred to as distributed parameter systems. System modeling and simulation are key steps in the in-depth analysis and manipulation of the process. Since the constitutive equations of space-time systems are usually partial differential equations, it is very difficult to model/simulate or control them.

• In off-line simulation analysis, the core issue is the parameter optimization of the system. Due to the complexity of the physical model described by partial differential equations, traditional optimization methods cannot be applied directly. According to the experience of experts and the specific characteristics of the system, an intelligent method of multi-objective mixing with parameter sensitivity selection and multi-scale calibration is proposed, which can improve the model tuning.


• Online performance prediction and control require fast analytical models. This requires the partial differential equation to be reduced in the ordinary differential equations. The most common method is the reduced order method based on space-time separation. However, under strong uncertainty and complex boundary conditions, more sensors need to be installed for satisfactory modelling. This requires constructing a more appropriate model structure for the specific characteristics of the process. At the same time, through intelligent learning, the effective information of the process is deeply explored for the optimal tuning of model parameters, thereby effectively improving the online fitting accuracy under reduced sensors.

In short, many methods in the field of machine learning can be applied to spatiotemporal modeling to improve the accuracy and adaptability of the model. The research method was initially applied to the analysis and prediction of the temperature field performance of automotive lithium batteries, as well as the curing process in IC packaging industry.

Biography: Han-Xiong LI 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 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 banking, industry, and academia. He has authored 2 books and about 20 patents, and published more than 200 SCI journal papers with h-index 43 (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 informatics. 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 scholar by the Ministry of Education, China in 2006, and a scholar 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.

Prof. Fang Tang, California State Polytechnic University Pomona, USA

Speech Title: Self-Driving Car: Past, Present and Future

Abstract: Autonomous vehicles are becoming more and more popular in both industry and academia because of the advancement of sensing technologies and software development. ABI Research estimates that by 2030, there will be 11 million driverless vehicles on the road globally – about 5 percent of the traffic.

This keynote speech will focus on answering the following three questions:

1. What were the driven forces of self-driving car?
2. What is the current state of art in this technology?
3. What are the hurdles and what can we expect in the future?

Particularly, we will have a focus on the key software requirements for a self-driving car, a survey on some of the current technologies being applied in this area, and some preliminary results of self-driving car development at Cal Poly Pomona, including: user interface design, path planning, localization, obstacle avoidance, and following traffic rules.

Biography: Dr. Fang Tang is the Chair and Professor in the Computer Science Department at California State Polytechnic University Pomona (Cal Poly Pomona). She joined Cal Poly Pomona in Fall 2006 as an Assistant Professor and was promoted to Associate Professor in 2012 and Professor in 2017. Dr. Tang received her PhD degree in Computer Science in 2006 from The University of Tennessee Knoxville (UTK), performing her research on multi-robot systems. She received her MS degree in Computer Science from UTK in 2003 and her BS degree in Computer Science from Sichuan University in 2000. Dr. Tang is the founder of the Intelligent Robotics Lab (IRLab) at Cal Poly Pomona. Her research interests include multi-robot systems, human-robot interaction, unmanned systems and educational robotics. Her dissertation research on ASyMTRe, a system for automating solution generation for multi-robot teams, is widely recognized by the multi-robot research community. In recent years, she extended her research in unmanned systems. In collaboration with faculty from the College of Engineering, she has obtained funding from the National Science Foundation and Northrop Grumman Corporation to support their research in unmanned technologies. She has over 30 referred journal, conference papers, book chapters and presentations. About 400 citations on Google Scholar.

Prof. Chun-Yi Su, Concordia University, Canada

Speech Title: Robust Control of Underactuated Mechanical Systems

Abstract: In recent years, there has been great theoretical and practical interest in controlling underactuated mechanical systems. These systems are defined as underactuated because they have more joints than control actuators. Much of this interest is a consequence of the importance of such systems in application. For example, underactuation may arise in free-flying space robots, underwater vehicles without base actuators, legged robots with passive joints, redundant robots with flexible components, and in many other practical applications. Furthermore, when one or more joints of a standard manipulator fail, it becomes an underactuated mechanism and needs a special control algorithm to continue operation; thus the development of a control technique for underactuated systems will increase the reliability and fault-tolerance of current and future robots. Interest in studying underactuated mechanical systems is also motivated by their role as a class of strongly nonlinear systems where complex internal dynamics, nonholonomic behavior, and lack of feedback linearizability are often exhibited. Traditional nonlinear control methods are insufficient in these cases and new approaches must be developed.

In this presentation, an entirely new method is discussed. A robust nonlinear control law is proposed for underactuated mechanical systems in the presence of parameter uncertainties. The development is based on variable structure theory. The main advantage of the presented scheme is that the uncertainty bounds, needed to design the control law and to prove globally asymptotic stability, depend only on the upper bounds of the inertia parameters. These upper bounds can easily be computed making a control law possible for complex underactuated systems.  Finally, the real-time application of this algorithm to a specific underactuated robot, Pendubot, is included to demonstrate the control performance.

Biography: Dr. Chun-Yi Su received his Ph.D. degrees in control engineering from South China University of Technology in 1990. After a seven-year stint at the University of Victoria, he joined the Concordia University in 1998, where he is currently a Professor of Mechanical and Industrial Engineering and holds the Concordia Research Chair in Control. His research covers control theory and its applications to various mechanical systems, with a focus on control of systems involving hysteresis nonlinearities. He is the author or co-author of over 400 publications, which have appeared in journals, as book chapters and in conference proceedings. In addition to his academic activities, he has worked extensively with industrial organizations on various projects. Dr. Su has been an Associate Editor of IEEE Transactions on Automatic Control, IEEE Transactions on Control Systems Technology, Mechatronics, Control Engineering Practice, and several other journals. He has served as Chair/Co-Chair for numerous international conferences.