Prof. Guanghong Yang (IEEE Fellow, CAA Fellow)

Northeastern University, China

Bio: Guang-Hong Yang received his B.S. and M.S. degrees in Mathematics, and Ph.D. degree in control theory and control engineering with Northeastern University, Shenyang, China, in 1983, 1986, and 1994, respectively. He is currently a chair professor and the dean with the College of Information Science and Engineering, Northeastern University. He is an IEEE Fellow and a Fellow of Chinese Association of Automation (CAA). Dr. Yang has been a general chair of the annual Chinese Control and Decision Conference (CCDC) since 2011, and is the Editor-in-Chief for the Journal of Control and Decision and an Editor for the International Journal of Control, Automation (IJCAS). His current research interests include fault-tolerant control, fault detection and isolation, safety of cyber-physical systems, and unmanned systems. He has published 3 monographs and over 400 papers in the international journals, and is a highly cited researcher (since 2014) selected by Elsevier and a highly cited researcher (since 2019) selected by Clarivate.

Title: Obstacle/Collision Avoidance in Distributed Optimal Coordination of Multiple Euler-Lagrangian Systems

Abstract: This talk studies the problem of obstacle/collision avoidance in distributed optimal coordination (DOC) for multiple uncertain Euler-Lagrangian (EL) systems. The main challenge focuses on the co-design of obstacle avoidance mechanism and distributed optimization strategy. To address it, a secure trajectory planning method is firstly proposed based on an online projection mechanism; then a novel safety barrier function in the closed form of path integrals is constructed which can adaptively adjust the secure trajectory tracking error to avoid the obstacles. Based on the Lyapunov method and boundedness analysis for the barrier function, it is proved that all the EL systems can simultaneously achieve the global convergence and obstacle avoidance. In order to prevent the collisions between agents, the proposed scheme is further extended to the collision-free DOC problem.

 

Prof. Rodolphe Sepulchre (IEEE Fellow, IFAC Fellow)

University of Cambridge, United Kingdom

Bio: Rodolphe Sepulchre received the engineering degree (1990) and the PhD degree (1994), both from the Université catholique de Louvain, Belgium. He was a postdoctoral research associate at the University of California, Santa Barbara, from 1994 to 1996. He was then appointed at the Université de Liège in 1997. In 2013, he moved to Cambridge, UK, where he holds the control chair in the Department of Engineering and a professioral fellowship in Sidney Sussex College. He held visiting positions at Princeton University (2002-2003), the Ecole des Mines de Paris (2009-2010), California Institute of Technology (2018), and part-time positions at the University of Louvain (2000-2011) and at INRIA Lille Europe (2012-2013). He was the Petar Kokotovic Distinguished Visiting Professor of UCSB in 2019.
He is a fellow of IFAC (2020), IEEE (2009), and SIAM (2015). In 2008, he received the IEEE Control Systems Society Antonio Ruberti Young Researcher Prize. He was elected at the Royal Academy of Belgium in 2013. He is the recipient of the 2020 IEEE Axelby Best Paper Award. He is (co-) author of the monographs Constructive Nonlinear Control (1997, with M. Jankovic and P. Kokotovic) and Optimization on Matrix Manifolds (2008, with P.-A. Absil and R. Mahony). His current research interests include the differential theory of nonlinear systems and the feedback control principles of neuronal circuits. He is a recipient of two ERC advanced grants (Switchlets (2015-2021) and SpikyControl (2022-2027)). He is Editor-in-Chief of the IEEE Control Systems Magazine since 2020.

Speech Title: Spiking Control Systems
Abstract: Spikes and rhythms organize control and communication in the animal world, in contrast to the bits and clocks of digital technology. As continuous-time signals that can be counted, spikes have a mixed nature. This talk will review ongoing efforts to develop a control theory of spiking systems. The central thesis is that the mixed nature of spiking results from a mixed feedback principle, and that a control theory of mixed feedback can be grounded in the operator theoretic concept of maximal monotonicity. As a nonlinear generalization of passivity, maximal monotonicity acknowledges at once the physics of electrical circuits, the algorithmic tractability of convex optimization, and the feedback control theory of incremental passivity. We discuss the relevance of a theory of spiking control systems in the emerging age of event-based technology.

 

Prof. Farshad Khorrami

New York University, USA

Bio: Farshad Khorrami received his bachelors degrees in Mathematics and Electrical Engineering in 1982 and 1984 respectively from The Ohio State University. He also received his Master's degree in Mathematics and Ph.D. in Electrical Engineering in 1984 and 1988 from The Ohio State University. Dr. Khorrami is currently a professor of Electrical & Computer Engineering Department at NYU where he joined as an assistant professor in Sept. 1988. His research interests include system theory and nonlinear controls, robotics, machine learning, cyber physical system security, autonomous unmanned vehicles, embedded system security, and large-scale systems and decentralized control. Prof. Khorrami has published more than 300 refereed journal and conference papers in these areas. His book on “modeling and adaptive nonlinear control of electric motors” was published by Springer Verlag in 2003. He also has fourteen U.S. patents on novel smart micro-positioners and actuators, embedded system security, and wireless sensors and actuators. He has developed and directed the Control/Robotics Research Laboratory at Polytechnic University (Now NYU) and Co-Director of the Center for Artificial Intelligence and Robotics at NYU Abu Dhabi.  His research has been supported by the Army Research Office, National Science Foundation, Office of Naval Research, DARPA, Dept. of Energy, Sandia National Laboratory, Army Research Laboratory, Air Force Research Laboratory, NASA, and several corporations. Prof. Khorrami has served as general chair and conference organizing committee member of several international conferences. He has also commercialized UAVs as well as development of auto-pilots for various unmanned vehicles.

Speech Title: Autonomous Vehicles: Resilient Sensor Fusion and Security

Abstract: The development of autonomous unmanned vehicle technologies and their deployment involves several core challenges in vehicle design, sensor data processing, data fusion, localization, navigation, world modeling, obstacle avoidance, path planning, collaborative mission planning and formation maneuvering, distributed sensing and monitoring, and control. Environment perception and autonomous navigation using real-time sensor data in uncertain environments is a crucial capability for robotic vehicles. To this end, this talk will focus on machine learning based approaches for autonomous navigation of ground vehicles in unknown environments. Specifically, an end-to-end learning framework for real-time fusion of raw sensor data from camera and LIDAR will be presented. Experimental studies on small unmanned vehicles (ground and aerial platforms) will be presented including analyses of the proposed methodology to various types of sensor noise/nonidealities, sensor failures, occlusions, and environment variations. While the proposed end-to-end learning approach provides these strong robustness properties, it will then be shown that specifically crafted perturbations (adversarial perturbations) both in camera and LIDAR data can still generate undesirable behaviors. Lastly, methods to alleviate such fragility of learning based systems to adversarial perturbations will be presented based on generative adversarial learning based techniques and control barrier functions.

 

Prof. Emily Cross

University of Glasgow, UK

Bio: Emily is a Professor of Social Robotics, based at the University of Glasgow’s Institute of Neuroscience and Psychology. She completed undergraduate studies in psychology and dance in California, followed by an MSc in cognitive psychology as a Fulbright Fellow in New Zealand, and a PhD in cognitive neuroscience at Dartmouth College. At Glasgow, she leads a vibrant research team who explores experience-dependent plasticity in the human brain and behaviour using neuroimaging, neurostimulation and behavioural techniques, and drawing upon expert dancers, robots and professional circus performers to address questions at the intersection of brain science, the arts and technology.

Speech Title: Social Neurocognitive Approaches to Informing and Improving Human—Robot Interactions

Abstract: Understanding how we perceive and interact with others is a core challenge of social cognition research. This challenge is poised to intensify in importance as the ubiquity of artificial intelligence and the presence of humanoid robots in society grows. My group’s research applies established theories and methods from psychology and neuroscience to questions concerning how people perceive, interact, and form relationships with robots. Robots provide a resolutely new approach to studying brain and behavioural flexibility manifest by humans during social interaction. As machines, they can deliver behaviours that can be perceived as “social”, even though they are artificial agents and, as such, can be programmed to deliver a perfectly determined and reproducible set of actions. As development of service robots, home companion robots and assistance robots for schools, hospitals and care homes continues apace, whether we perceive such machines as social agents and how we engage with them over the long term remains largely unexplored. My team’s research, which bridges social cognition, neuroscience and robotics, has important implications not only for the design of social robots, but equally critically, for our understanding of the neurocognitive mechanisms supporting human social behaviour more generally.

Prof. Guoyan Yu

Guangdong Ocean University, China

Bio: Guoyan Yu received her Ph.D. degree from the School of Mechanical & Automotive Engineering, South China University of Technology, Guangzhou, China, in 2001. Currently, she is a Professor in the School of Mechanical Engineering, Guangdong Ocean University, Zhanjiang, China. Her research interests include intelligent design and manufacturing, intelligent marine fishery aquaculture supporting equipment, and manufacturing informatization. She focuses on the application of robotics and artificial intelligence technology in the field of mechanical engineering. Over the years, she has published more than 30 academic papers and has been authorized 8 invention patents, 17 utility model patents and 3 software copyrights as the first inventor. Moreover, she has presided over 17 scientific research projects, such as "intelligent deep-sea fishery farming equipment" and "semiopen new piezoelectric material driving and sensing flexible bionic deep-sea robot".

Speech Title: Marine robot control for deep-water cage culture

Abstract: To improve the unmanned and automatic degree of deep-water cage culture and reduce breeding costs, marine robots are widely used, such as remotely operated vehicles (ROVs), autonomous underwater vehicles (AUVs), and marine surface vehicles (MSVs). Because the marine environment is extremely bad, higher requirements are put forward for the control performance of the robot, and underactuated and nonlinear characteristics make the design of the robot controller more challenging. Therefore, the finite-time prescribed performance trajectory tracking control method, predefined-time nonsingular fast terminal sliding mode control method, and neural network-based prescribed performance adaptive finite-time formation control method are proposed to obtain satisfactory performances. Neural networks, the barrier Lyapunov function, prescribed performance control, and dynamic surface control are developed to improve the robustness and precision of controllers. Theorem analysis shows the stability of the closed-loop system. Simulations and experiments demonstrate the effectiveness of these proposed methods.