CACRE 2023 Speakers
Prof. Hugh H.T. Liu
University of Toronto, Canada
Plenary Lecture: Coordinated control for Autonomous Drone Sampling Applications
Abstract: In this talk, we will present recent research and development projects involving multi-unmanned aerial systems for several autonomous missions. The focus is placed on the aerodynamic impact and the corresponding control solutions. Based on our investigation, the major conclusion is drawn to demonstrate the promising feature of integrated flight dynamics and control.
Biography: Hugh H.T. Liu is a Professor of the University of Toronto Institute for Aerospace Studies, where he has been on the faculty since 2000. He currently also serves as the Director of Natural Science and Engineering Research Council of Canada (NSERC) Collaborative Research and Training Experience (CREATE) Program on Unmanned Aerial Vehicles and Centre for Aerial Robotics Research and Education. In 2022, he was named as the fellow of Canadian Academy of Engineering. The Canadian Aeronautics and Space Institute (CASI) Senior Awards recognizes Canadians for their outstanding achievement in aeronautics, space and related technologies. For the 2021 Senior Awards, Professor Hugh H.T. Liu was awarded the CASI McCurdy Award, presented for his outstanding achievement in the science and creative aspects of engineering relating to aeronautics and space research.
Prof. Jie Chen (IEEE Fellow)
The City University of Hong Kong, China
Plenary Lecture: The Undying PID Control: From Folklores to an Explainable Theory
Abstract: For well over a century, PID control stood out as the most favored method for its simplicity, robustness, ease of implementation and cost-effectiveness. It continues to demonstrate its sustained power and inexplicable charm, serving an awe-inspiring testimony to its incredible vitality and widespread acceptance by industrial control and automation communities. Traditionally, the design and implementation of PID controllers are conducted by somewhat ad hoc, trial and error tuning methods. One must then wonder, with its seemingly simplistic structure and empirical rules of design, why on earth PID control performs so well? How good can it get to be? Would it continue to be relevant in an AI-centric era? In this talk I shall attempt to provide an anecdote and an analysis to these contemplations, using system robustness--arguably the most important goal in feedback design--as a pilot problem. We address such canonical problems as gain, phase, and delay margins, which define in different manners the largest range of unknown, variable parameters that a system can tolerate to maintain stability robustness. I shall present our recent triumphs in determining these margins achievable by PID control, and argue in favor of a least amount of PID insight vis-a-vis massive AI computations. The solutions to the problems, much to our delight, provide analytical justifications to folk wisdom of one hundred years on PID controller tuning and design.
Biography: Dr. Chen currently holds the appointment of Chair Professor of Electronic Engineering at City University of Hong Kong, Hong Kong, China. Prior to joining City University, he was with School of Aerospace Engineering and School of Electrical and Computer Engineering at Georgia Institute of Technology, Atlanta, Georgia from 1990 to 1993. He joined University of California, Riverside, California as an Assistant Professor in 1994, where he became an Associate Professor in the Department of Electrical Engineering in 1997, and a Professor in 1999. He served as the Professor and Chair of Electrical Engineering from 2001 to 2006. He has also held guest positions and visiting appointments in several universities, such as Zhejiang University, Tokyo Institute of Technology,etc. His main research interests are in the areas of networked control and information theory, multi-agent systems, time-delay systems, linear multivariable systems theory, system identification, robust control, and in broad applications of control theory and techniques. He is the author of several books, among which are Control-Oriented System Identification: An H-infinity Approach (with G. Gu, Wiley-Interscience, 2000), Stability of Time-Delay Systems (with K. Gu and V.L. Kharitonov, Birkhauser, 2003), Towards Integrating Control and Information Theories: From Information-Theoretic Measures to Control Performance Limitations (with Song Fang and Hideaki Ishii, Springer, 2016) and Limits of Stability and Stabilization of Time-Delay Systems: A Small-Gain Approach (with Jing Zhu, Tian Qi and Dan Ma, Springer, 2018).
Prof. Kenji Fujimoto
Kyoto University, Japan
Keynote Lecture: Passivity based sliding mode control for electro-mechanical systems
Abstract: Both passivity based control and sliding mode control are well established useful techniques for nonlinear systems. Although they have been considered to be completely incompatible methods, there are actually some points of contact between them. This presentation shows recent results on their integration based on the port-Hamiltonian framework. Passivity based control is a method to find energy based Lyapunov function candidates for physical systems. It can generate Lyapunov functions for several objectives such as trajectory tracking control and/or those for output feedback control by a similar way to the standard artificial potential function method. On the other hand, sliding mode control is a technique to design a closed loop system with fast convergence by employing discontinuous high gain feedback without constructing any explicit Lyapunov function for the closed loop system. This presentation gives a brief summary on the author's developments on passivity based control with a recent attempt to integrate the two different techniques by employing a special class of non-smooth Lyapunov functions. This idea allows for highly flexible controller design that smoothly complements passivity based control and sliding mode control. Furthermore, it will also cover how the proposed framework is applied to electro-mechanical systems as well as conventional mechanical ones.
Biography: Kenji Fujimoto received his B.Sc. and M.Sc. degrees in Engineering and Ph.D. degree in Informatics from Kyoto University, Japan, in 1994, 1996 and 2001, respectively. He is currently a professor of Graduate School of Engineering, Kyoto University, Japan. From 1997 to 2004 he was a research associate of Graduate School of Engineering and Graduate School of Informatics, Kyoto University, Japan. From 2004 to 2012 he was an associate professor of Graduate School of Engineering, Nagoya University, Japan. From 1999 to 2000 he was a research fellow of Department of Electrical Engineering, Delft University of Technology, The Netherlands. He has held visiting research positions at the Australian National University, Australia and Delft University of Technology, The Netherlands, and RIKEN, Japan. He received The IFAC Congress Young Author Prize in IFAC World Congress 2005 and SICE Control Division Pioneer Award in 2007. He served as associate editors of IEEE Transactions on Automatic Control, IEEE CSS Conference Editorial Board, and SICE Journal of Measurement, Control, and System Integration. His research interests include nonlinear control and stochastic systems theory.
Prof. Shaojie Shen
The Hong Kong University of Science and Technology, Hong Kong, China
Keynote Lecture: TBA
Biography: Prof Shaojie Shen received his BEng degree in Electronic Engineering (Honors Research Option) from the Hong Kong University of Science and Technology in 2009. He received his MS in Robotics and PhD in Electrical and Systems Engineering in 2011 and 2014, respectively, all from the University of Pennsylvania. He joined the Department of Electronic and Computer Engineering at the Hong Kong University of Science and Technology in September 2014 as an Assistant Professor. His research interests are in the areas of robotics and unmanned aerial vehicles, with focus on state estimation, sensor fusion, localization and mapping, and autonomous navigation in complex environments. His work has been covered by major media outlets such as TED, ABC, The New Yorker, and Discovery Channel.
Prof. Zhengkun Yi
Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, China
Invited Lecture: Biomimetic Tactile Sensing: from Humans to Robots
Abstract: As robots move from laboratories to domestic environments, they will be required to perform manipulation tasks in unstructured environments. Such robots must be able to achieve sophisticated interactions with the environment and to perform complex tasks such as grasping objects with arbitrary unknown shapes. The sense of touch is a fundamental capability of robots to achieve these tasks. How to endow robots with human-like tactile abilities has attracted increasing attention in both academia and industry. In recent years, tactile sensors such as BioTac, GelSight, and TacTip have surpassed human tactile perception capabilities in terms of resolution, response speed, and measurement range, but there is still a big challenge to achieve human-like tactile sensation. One of the main difficulties is how to achieve precise understanding and efficient use of tactile signals. This talk will first introduce the human tactile perception and technologies of robotic tactile perception, and then cover the recent research progress including bionic tactile perception, sensor drift compensation, and robot tactile interaction based on artificial intelligence and brain-like intelligence methods.
Biography: Zhengkun Yi is currently a full professor in Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences. He received the B.S. degree from University of Science and Technology of China (USTC) the M.E. degree from Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences and the Ph.D. degree from Nanyang Technological University (NTU) and Technische Universitaet Darmstadt (TUD). He was a postdoctoral fellow at Nanyang Technological University and National University of Singapore. His research interests include tactile sensing, robot learning, machine learning computer vision, etc. He has authored/co-authored more than 30 peer reviewed journal and conference papers, including IEEE-TIE, IEEE-TSMC, IEEE-TASE, etc. He received the NSFC excellent young scholar (overseas), the best student paper award in IEEE RCAR 2022. He is an associate editor for IEEE RAL and serves as publicity chair of IEEE RCAR 2021.
Prof. Wencen Wu
San Jose State University, USA
Invited Lecture: Multi-robot exploration of spatial-temporal varying fields
Abstract: The exploration of unknown spatial-temporal varying fields, especially in areas that are inaccessible or hostile to humans, presents a fundamental and challenging problem in the field of mobile robots. In such scenarios, mobile sensor networks consisting of coordinated groups of mobile robots offer promising solutions. This talk presents the co-design of distributed sensing and cooperative control algorithms for mobile sensor networks, specifically tailored for the exploration of unknown spatial-temporal varying fields. The speaker will present online state estimation and parameter identification algorithms designed for distributed parameter systems using mobile sensor networks with reduced computational and communication costs. Various applications will be discussed, including source seeking, boundary following, and map reconstruction. The potential applications of this work extend to areas where resources are constrained, such as search and rescue operations, chemical contamination localization, and wildfire detection and monitoring.
Biography: Wencen Wu is an Associate Professor in the Computer Engineering Department at San Jose State University. Prior to joining SJSU in Fall 2018, she was an Assistant Professor in the ECSE department of Rensselaer Polytechnic Institute. She received her Ph.D. and M.S. from the Georgia Institute of Technology, and the dual-M.S. and B.S. from Shanghai Jiao Tong University. Her research interests include cooperative control and sensing for multi-robot systems and state estimation and parameter identification for distributed parameter systems in the senarios of multi-robot exploration in complex environments with limited resources. She has published over 50 peer-reviewed research papers on journals and conferences in the areas of systems and control theory and robotics. She is the PI of several U.S. National Science Foundation (NSF) funded projects. She has served as program committee member for many IEEE international conferences.
Prof. Fei Miao
University of Connecticut, USA
Invited Lecture: Learning and Control for Safety, Efficiency, and Resiliency of Cyber-Physical Systems
Abstract: The rapid evolution of ubiquitous sensing, communication, and computation technologies has contributed to the revolution of cyber-physical systems (CPS). Learning-based methodologies are integrated to the control of physical systems and provide tremendous opportunities for AI-enabled CPS. However, existing networked CPS decision-making frameworks lack understanding of the tridirectional relationship among communication, learning and control. It remains challenging to leverage the communication capability for the learning and control methodology design of CPS, to improve the safety, efficiency, and robustness of the system. In the first part of the talk, we present our research contributions on learning and control with information sharing for networked CPS. We design the first uncertainty quantification method for collaborative perception of connected autonomous vehicles (CAVs) and show the accuracy improvement and uncertainty reduction performance of our method. To utilize the information shared among agents, we then develop a safe and scalable deep multi-agent reinforcement learning (MARL) algorithms to improve system safety and efficiency. We validate the benefits of communication in MARL especially for CAVs under challenging mixed traffic scenarios. To motivate agents to communicate and coordinate, we design a novel stable and efficient Shapley value-based reward reallocation scheme for MARL. In the second part of the talk, we briefly present our research contributions on data-driven robust optimization for autonomous mobility-on-demand (AMoD) systems and CPS security.
Biography: Fei Miao is an Assistant Professor of the Department of Computer Science & Engineering, a Courtesy Faculty of the Department of Electrical & Computer Engineering, University of Connecticut since 2017. She is also affiliated to the Institute of Advanced Systems Engineering and Eversource Energy Center. She will be promoted to associate professor with tenure in Fall 2023. She was a postdoc researcher at the GRASP Lab and the PRECISE Lab of the University of Pennsylvania from 2016 to 2017. She received the Ph.D. degree and the Best Doctoral Dissertation Award in Electrical and Systems Engineering, with a dual M.S. degree in Statistics from the University of Pennsylvania in 2016. She received the B.S. degree in Automation from Shanghai Jiao Tong University. Her research focuses on multi-agent reinforcement learning, robust optimization, uncertainty quantification, and control theory, to address safety, efficiency, robustness, and security challenges of cyber-physical systems, for application areas such as connected autonomous vehicles, intelligent transportation systems, transportation decarbonization, and smart cities. Dr. Miao is a receipt of the NSF CAREER award and a couple of other awards from NSF and DOE. She received the Best Paper Award and Best Paper Award Finalist at the 12th and 6th ACM/IEEE International Conference on Cyber-Physical Systems (ICCPS) in 2021 and 2015, Best paper Award at the 2023 AAAI DACC workshop, respectively.
CACRE Past Speakers
Prof. Peter Corke
The Queensland University of Technology, Australia
Prof. Seth Hutchinson
Georgia Institute of Technology, USA
Prof. Dan Zhang
York University, Canada
Prof. Feng Gao
Shanghai Jiaotong University, China
Prof. Rong Xiong
Zhejiang University, China
Prof. Elizabeth Croft
University of Victoria, Canada
Prof. Silvia Ferrari
Cornell University, USA
Prof. Bin Zi
Hefei University of Technology, China
Prof. Dongbin Zhao
Chinese Academy of Sciences, China
Prof. Iain D. Couzin
University of Konstanz, Germany
Prof. Genci Capi
Hosei University, Japan
Prof. Yang Shi
University of Victoria, Canada
Prof. Jiancheng Yu
Shenyang Institute of Automation, Chinese Academy of Sciences, China
Prof. Michael Y. Wang
Hong Kong University of Science and Technology, HKSAR, China
Prof. Guangren Duan
Harbin Institute of Technology, China
Prof. Yiming Rong
Southern University of Science and Technology of China, China
Prof. Du Ruxu
South China University of Technology, China
Prof. Ya-Jun Pan
Dalhousie University, Canada
Prof. Wenqiang Zhang
Fudan University, China
Prof. Jonathan Wu
University of Windsor, Canada
Prof. Fumin Zhang
Georgia Institute of Technology, USA
Prof. Xianbo Xiang
Huazhong University of Science and Technology, China
Prof. Sebastian Scherer
Prof. Xuechao Duan
Xidian University, China
Prof. Xiaoli Bai
Rutgers,The State University of New Jersey, USA
Prof. Xianping Fu
Dalian Maritime University, China
Prof. Zhufeng Shao
Tsinghua University, China
Prof. Yifei Pu
Sichuan University, China
Dr. Simon K.S. Cheung
Open University of Hong Kong, HKSAR, China
Prof. Wei Zhang
Southern University of Science and Technology, China
Prof. Bin Li
Sichuan University, China
Czech Technical University in Prague, Czech Republic
Prof. Hongde Qin
Harbin Engineering University, China
Prof. Ye Yuan
Huazhong University of Science and Technology, China
Prof. Bo Li
Xi'an Jiaotong University, China