Yong-duan Song, Chongqing University, China
On the “GERA” features of neuroadaptive control and robust adaptive control
Most practical engineering systems are highly nonlinear and complex in nature, posing significant technical challenge for control design. The two typical well-known control methods to deal with system nonlinearities and uncertainties are Neural Network (NN)-based Control and Robust Adaptive Control. Naturally, an interesting question worthy of examining is: “for a given dynamic system with modeling uncertainties and external disturbances, would the neural network based control outperform other type of control such as robust adaptive control?” This talk will explicitly address such issue from analytical and experimental perspectives. The focus will be on comparing the “GEAR” features between those two types of control schemes, i.e., generality, effectiveness, affordability and reliability, in addition to user-friendliness and simplicity.
Bio: Yong-duan Song received his Ph.D. degree in Electrical and Computer Engineering from Tennessee Technological University, Cookeville, USA, in 1992. He held a tenured Full Professor position with North Carolina A&T State University, Greensboro, from 1993 to 2008 and a Langley Distinguished Professor position with the National Institute of Aerospace, Hampton, VA, from 2005 to 2008.
He is now the Dean of School of Automation, Chongqing University, China and the Founding Director of the Institute of Smart Systems and Renewable Energy, Chongqing University. He was one of the six Langley Distinguished Professors with the National Institute of Aerospace (NIA), and Founding Director of Cooperative Systems at NIA. He has served as an Associate Editor/Guest Editor for several prestigious scientific journals, including IEEE Trans. On Automatic Control, IEEE Trans. On Neural Networks and Learning Systems, IEEE Trans. On Intelligent Transportation Systems, IET Control Theory and Applications and others.
Prof. Song has received several competitive research awards from the National Science Foundation, the National Aeronautics and Space Administration, the U.S. Air Force Office, the U.S. Army Research Office, and the U.S. Naval Research Office. His research interests include intelligent systems, guidance navigation and control, bio-inspired adaptive and cooperative systems, rail traffic control and safety, and smart grid.
Jian S Dai, IEEE Fellow, King’s College London, UK
Robots of the Future That Are Shaped by Arts and Nature
This plenary talk gives a philosophical view over the entwinement between robotics and arts, presents a new doctrine that innovative robotics could be shaped by the arts, and provides an intrinsic connection between arts and robot development, leading to decades of development in origami robots, arts robots, metamorphic robots, rehabilitation robots and reconfigurable robots. This entwinement is elevated by mathematical tools, particularly the advanced kinematics with screw theory and its relations to Lie groups and Lie algebra through finite screws. With change of the order of a screw system, a robot mechanism changes its mobility and presents its different topologies for variable tasks.
The talk further presents various case-studies to reveal how the inspiration and aspiration were absorbed from arts and nature via advanced kinematics and how robot creation and innovation were made through this doctrine. The talk will then give a large number of applications of the evolutionary and reconfigurable robotic mechanisms in assembly, packaging, food industry, domestic robots, rehabilitation and manufacture, leading to Robots of Future in the decades ahead.
Bio: Professor Jian S. Dai, CEng, IEEE Fellow, ASME Fellow, IMechE Fellow, is Chair of Mechanisms and Robotics at King’s College London and a pioneer in reconfigurable mechanisms and robots, in origami robots, in ankle rehabilitation robots and in metamorphic robots. He established the field of reconfigurable mechanisms and the sub-field of metamorphic mechanisms in robotics, a concept that could bridge the gap between versatile but expensive robots, and efficient but non-flexible machines, and their applications to health, home and manufacture.
Professor Dai received a BEng in 1982 and an MSc in 1984 from Shanghai Jiao Tong University, and received a PhD in Advanced Kinematics and Robotics from the University of Salford in the UK in 1993.
Professor Dai is the recipient of 2015 ASME Mechanisms and Robotics Award that is an honor to engineers and scientists who have made a lifelong contribution to the fundamental theory, design and applications of mechanisms and robotic systems. He is the 27th recipient since the award was established in 1974. Professor Dai received many other awards including 2010 Overall Supervisory Excellence Award by King’s College London, 2012 ASME Outstanding Service Award and 2012 Mechanisms Innovation Award, together with several conference and journal Best Paper awards.
Professor Dai has published three books and over 500 peer-reviewed journal and conference papers, with a large number of citations and served as Subject Editor/Associate Editor of several prestigious journals including Mechanism and Machine Theory, ASME Journal of Mechanisms and Robotics, IEEE Transactions on Robotics and Journal of Mechanical Engineering Science. He was awarded a large number of Research Council grants including EPSRC, EU, NSFC, and industrial grants and has educated over 25 PhD students with 10 former PhD students working in ten leading universities in the UK, Mexico, Australia, Italy, UAE and China.
Yang Shi, Fellow of IEEE, ASME, CSME
University of Victoria, Canada
A Robust Model Predictive Control (MPC) Framework for Intelligent Mechatronic Systems
Networked and distributed control for mechatronic systems have received great attention in the control community due to its wide application areas. Network-induced limitations may be caused by the presence of a communication channel, or because of the efficient assignment of power and other limited resources. Intelligent mechatronic systems represent a large class of smart systems that encompass computational (i.e., hardware and software) and physical components, seamlessly integrated and closely interacting to autonomously sense and manipulate the changing state of the physical system. These systems involve a high degree of complexity at numerous spatial and temporal scales and highly networked communications integrating computational and physical components. Model predictive control (MPC) is a promising paradigm for high-performance and cost-effective control of networked and distributed mechatronic systems. This talk will firstly summarize the major application requirements and challenges to innovate in designing, implementing, deploying and operating intelligent mechatronic systems. Further, the robust MPC and distributed MPC design methods will be presented. Finally, the application of MPC algorithms to intelligent autonomous under water vehicles (AUV) will be illustrated.
Bio: Yang SHI received the Ph.D. degree in electrical and computer engineering from the University of Alberta, Edmonton, AB, Canada, in 2005. From 2005 to 2009, he was an Assistant Professor and Associate Professor in the Department of Mechanical Engineering, University of Saskatchewan, Saskatoon, Saskatchewan, Canada. In 2009, he joined the University of Victoria, and now he is a Professor in the Department of Mechanical Engineering, University of Victoria, Victoria, British Columbia, Canada. His current research interests include networked and distributed systems, model predictive control (MPC), cyber-physical systems (CPS), robotics and mechatronics, navigation and control of autonomous systems (AUV and UAV), and energy system applications.
Dr. Shi received the University of Saskatchewan Student Union Teaching Excellence Award in 2007. At the University of Victoria, he received the Faculty of Engineering Teaching Excellence in 2012, and the Craigdarroch Silver Medal for Excellence in Research in 2015. He received the JSPS Invitation Fellowship (short-term), and was a Visiting Professor at the University of Tokyo during Nov-Dec 2013. His co-authored paper was awarded the 2017 IEEE Transactions on Fuzzy Systems Outstanding Paper Award. He received the Humboldt Research Fellowship for Experienced Researchers in 2018. He is the founding Vice Chair of IEEE IES Technical Committee on Industrial Cyber-Physical Systems. Currently, he is Co-Editor-in-Chief for IEEE Transactions on Industrial Electronics; he also serves as Associate Editor for Automatica, IEEE Trans. Control Systems Technology, IEEE/ASME Trans. Mechatrnonics, IEEE Trans. Cybernetics, IET Control Theory and Applications, ASME Journal of Dynamic Systems, Measurement, and Control. He is a Fellow of IEEE, ASME and CSME, and a registered Professional Engineer in British Columbia, Canada.