Prof. Chun-Yi Su, Concordia University, Canada

Dr. Chun-Yi Su received his Ph.D. degree in control engineering from the South China University of Technology in 1990. After a seven-year stint at the University of Victoria, he joined Concordia University in 1998, where he is currently a Professor and Honorary Concordia University Research Chair. His research covers control theory and its applications to various mechanical systems, with a recent focus on modeling and control of soft robots. He is the author or co-author of over 500 publications, which have appeared in journals, as book chapters and in conference proceedings. He has been identified as Highly Cited Researchers from Clarivate since 2019.

Dr. Su has served as Associate Editor for several journals, including IEEE Transactions on Automatic Control, IEEE Transactions on Control Systems Technology, IEEE Transactions on Cybernetics, and other journals. He is a Distinguished Lecturer of IEEE RA Society. He served for many conferences as an Organizing Committee Member, including the General Chairs and Program Chairs.

Speech Title: Modeling and Parameters Learning of Dielectric Elastomer Enabled Soft Robots via Data-In-Loop Approach

Abstract: This talk describes the basic research in the new area of bioinspired soft robotics, with an emphasis on modeling and parameters learning. The developments in soft robotic research incorporate smart materials, flexible electronics, and 3D printing to emulate both the physical flexibility and the versatility of animals such as fish, caterpillars, snakes, and worms. Potentially applications include tissue engineering and biology; control engineering; additive manufacturing; biomedical engineering; medical devices. This talk is intended to discuss a newly proposed modeling and parameters learning of dielectric elastomer enabled soft robots via data-in-loop approach, showing the status of the art of the current research for modeling of the smart-material based soft robots.

Prof. Simon X. Yang, University of Guelph, Canada

Prof. Simon X. Yang received the B.Sc. degree in engineering physics from Beijing University,China in 1987, the first of two M.Sc.  degrees in biophysics from Chinese Academy of Sciences, Beijing, China in 1990, the second M.Sc. degree in electrical engineering from the University of Houston, USA in 1996, and the Ph.D. degree in electrical and computer engineering from the University of Alberta, Edmonton, Canada in 1999. Prof. Yang joined the School of Engineering at the University of Guelph, Canada in 1999. Currently he is a Professor and the Head of the Advanced Robotics & Intelligent Systems (ARIS) Laboratory at the University of Guelph in Canada.  Prof. Yang has diversified research expertise. His research interests include intelligent systems, robotics, control systems, sensors and multi-sensor fusion, wireless sensor networks, intelligent communications, intelligent transportation, machine learning, and computational neuroscience. He has published over 550 academic papers, including over 350 journal papers. Prof. Yang he has been very active in professional activities. Prof. Yang has served as the Editor-in-Chief of Intelligence & Robotics, and some other journals; and an Associate Editor of IEEE Transactions on Cybernetics, IEEE Transactions on Artificial Intelligence, and several other journals. He has been involved in the organization of many international conferences.

Speech Title: Bio-inspired Intelligent Formation Control of MultipleAutonomous Underwater Vehicles

Abstract: Research on biologically inspired intelligence has made significant progress in both understanding biological systems and developing bionic engineering applications to robotics and control systems. Formation control of autonomous underwater vehicle (AUV) fleets has an increasingly wide range of applications and has attracted much attention in the past few years. Due to its interdisciplinary nature, synthesis of high-performance AUV formation systems is still faced with numerous challenges and obstacles, both theoretically and practically.  This talk will focus on several robust and intelligent formation control methods for a fleet of multiple AUVs. Firstly, a novel distributed bio-inspired sliding mode formation protocol is developed to address the robust consensus formation tracking problem. This formation protocol considers the synchronization errors between neighboring AUVs and accounts for the impacts of ocean waves, currents, and uncertain hydrodynamic effects. Secondly, a robust learning-based approach is introduced to handle scenarios where nominal models of AUVs are unavailable for formation control design. A rigorous analysis is provided to ensure the feasibility of the method. After that, an innovative on-line motion optimization procedure is developed to tackle robust constrained consensus formation tracking. More precisely, this designed motion optimizer allows to generate coordination commands that not only satisfy the constraints enforced (e.g., velocities) but also dynamically optimize coordination performance.  Finally, a stability criterion is developed responsible for the impact of non-uniform communication delays in coordination, and in addition, a new online optimal control approach is proposed to resolve robust control with motion constraints.

Prof. Yajun Liu, South China University Of Technology, China

Prof. Yajun Liu was born on September 20, 1974 in Jiangxi, China. Native speaker of Chinese, fluent in English. His Education and Academic Research Experiences is as follows:
December, 2016- Now Professor in South China University of Technology School of Mechanical and Automotive Engineering.
December, 2009- December, 2010. Visiting Professor in Fluid Power Research Center (FPRC) Purdue University at West Lafayette, USA.
Feb, 2005 – July, 2016. Post-doctoral Research Fellow, Tokheim JV company in China.
June, 2002 Ph. D. in Mechanical Engineering. South China University of Technology, Guangzhou,China.
His research interests include Digital signal processing technology and its application in mechanical systems (such as hydraulic System for Energy Saving.); Intelligence control and Manufacturing
Engineering. Moreover, Prof. Yajun Liu has published more than 270 papers in Journals and proceedings of international conferences. 40+ patents on Mechanical System design and manufacturing.

Speech Title：Application scenario-driven intelligent process system R&D and Industrialization
Abstract： AI Technology is important in Intelligent Manufacturing process and also a huge challenge for researchers and engineers. In this presentation, we will make topics focusing on Intelligent Controller and its Application on manufacturing process and promoted it industrially in the industry and  using AI Technology to Optimized Design and Manufacturing of Mechanical Components etc.