Keynotes

Miroslav Krstić: Prescribed-Time Extremum Seeking for Mobile Robots and PDEs

Sr. Assoc. Vice Chancellor for Research
Director, Cymer Center for Control Systems and Dynamics
Daniel L. Alspach Endowed Chair in Dynamical Systems and Control
Distinguished Professor of Mechanical and Aerospace Engineering
Affiliate Distinguished Professor of Electrical and Computer Engineering
Affiliate Distinguished Professor of Mathematics
UNIVERSITY OF CALIFORNIA, SAN DIEGO

Short biography

Miroslav Krstic is Distinguished Professor of Mechanical and Aerospace Engineering, holds the Alspach endowed chair, and is the founding director of the Cymer Center for Control Systems and Dynamics at UC San Diego. He also serves as Senior Associate Vice Chancellor for Research at UCSD. As a graduate student, Krstic won the UC Santa Barbara best dissertation award and student best paper awards at CDC and ACC. Krstic has been elected Fellow of seven scientific societies – IEEE, IFAC, ASME, SIAM, AAAS, IET (UK), and AIAA (Assoc. Fellow) – and as a foreign member of the Serbian Academy of Sciences and Arts and of the Academy of Engineering of Serbia. He has received the Richard E. Bellman Control Heritage Award, SIAM Reid Prize, ASME Oldenburger Medal, Nyquist Lecture Prize, Paynter Outstanding Investigator Award, Ragazzini Education Award, IFAC Nonlinear Control Systems Award, Chestnut textbook prize, Control Systems Society Distinguished Member Award, the PECASE, NSF Career, and ONR Young Investigator awards, the Schuck (’96 and ’19) and Axelby paper prizes, and the first UCSD Research Award given to an engineer. Krstic has also been awarded the Springer Visiting Professorship at UC Berkeley, the Distinguished Visiting Fellowship of the Royal Academy of Engineering, the Invitation Fellowship of the Japan Society for the Promotion of Science, and four honorary professorships outside of the United States. He serves as Editor-in-Chief of Systems & Control Letters and has been serving as Senior Editor in Automatica and IEEE Transactions on Automatic Control, as editor of two Springer book series, and has served as Vice President for Technical Activities of the IEEE Control Systems Society and as chair of the IEEE CSS Fellow Committee. Krstic has coauthored fifteen books on adaptive, nonlinear, and stochastic control, extremum seeking, control of PDE systems including turbulent flows, and control of delay systems.

Abstract

Prescribed-time control (PTC) has emerged in 2017 as an interesting alternative to sliding mode control (SMC) for achieving convergence in finite time. While the finite time of convergence with SMC depends on the system’s initial condition, with PTC the convergence time can be set arbitrarily by the user. This is achieved by replacing the signum (discontinuous) feedback by time-varying feedback, with a gain that grows to infinity as the time approaches the terminal (prescribed) time of convergence, as in optimal control with a hard terminal constraint, encountered in classical Proportional Navigation control law in aerospace applications. I will present results, achieved over the past year – 2021 – by two of my students, Cemal Tugrul Yilmaz and Velimir Todorovski on extending PTC from stabilization and regulation problems to real-time optimization, namely, extremum seeking. Todorovski solves the problem of source seeking for mobile robots in GPS-denied environments. Yilmaz solves the problem of real-time optimization under large delays on the input and in the presence of PDE (partial differential equation) dynamics. Their designs are model-free and, most importantly, achieve convergence/optimality in a user-prescribed interval of time, independent of initial conditions. 

Frede Blaabjerg: Power Electronics Technology – Quo Vadis

Professor

Department of Electrical Engineering

Aalborg University Aalborg,

Denmark

Short biography

Frede Blaabjerg (S’86–M’88–SM’97–F’03) was with ABB-Scandia, Randers, Denmark, from 1987 to 1988. From 1988 to 1992, he got the PhD degree in Electrical Engineering at Aalborg University in 1995. He became an Assistant Professor in 1992, an Associate Professor in 1996, and a Full Professor of power electronics and drives in 1998. From 2017 he became a Villum Investigator. He is honoris causa at University Politehnica Timisoara (UPT), Romania and Tallinn Technical University (TTU) in Estonia.
His current research interests include power electronics and its applications such as in wind turbines, PV systems, reliability, harmonics and adjustable speed drives. He has published more than 600 journal papers in the fields of power electronics and its applications. He is the co-author of four monographs and
editor of ten books in power electronics and its applications. He has received 33 IEEE Prize Paper Awards, the IEEE PELS Distinguished Service Award in 2009, the EPE-PEMC Council Award in 2010, the IEEE William E. Newell Power Electronics Award 2014, the Villum Kann Rasmussen Research Award 2014, the Global Energy Prize in 2019 and the 2020 IEEE Edison Medal. He was the Editor-in-Chief of the IEEE TRANSACTIONS ON POWER ELECTRONICS from 2006 to 2012. He has been Distinguished Lecturer for the IEEE Power Electronics Society from 2005 to 2007 and for the IEEE Industry Applications Society from 2010 to 2011 as well as 2017 to 2018. In 2019-2020 he served as a President of IEEE Power Electronics Society. He has been Vice- President of the Danish Academy of Technical Sciences.
He is nominated in 2014-2020 by Thomson Reuters to be between the most 250 cited researchers in Engineering in the world.

Abstract

The world is becoming more and more electrified combined with that the consumption is steadily increasing – at the same time there is a large transition of power generation from fossil fuel to renewable energy based which all together challenges the modern power system but also gives many opportunities. We see also now big steps being taken to electrify the transportation – both better environment as well as higher efficiency are driving factors. One of the most important technologies to move this forward is the power electronics technology which has been emerging for decades and still challenges are seen in the technology and the applications it is used. This presentation will be a little forward looking (Quo Vadis) in some exciting research areas in order further to improve the technology and the systems it is used in. Following main topics will be discussed

  • The evolution of power devices
  • Renewable Generation
  • Reliability in power electronics
  • Power Electronic based Power System stability

At last some discussions about other hot topics will be given.

Haris Gačanin: Learning mechanism in next-generation wireless access

Professor

Faculty of Electrical Engineering and Information Technology

RWTH Aachen University

Germany

Short biography

Haris Gačanin (IEEE F’20) received his Dipl.-Ing. degree in Electrical engineering from the  University of Sarajevo in 2000. In 2005 and 2008, respectively, he received MSc and Ph.D. from Tohoku  University in Japan. He was with Tohoku University from 2008 until 2010, first as a Japan Society for the  Promotion of Science (JSPS) postdoctoral fellow and later as an Assistant Professor. He joined Alcatel 

Lucent Bell (now Nokia Bell) in 2010 as a Physical-layer Expert and later moved to Nokia Bell Labs as  Department Head. Since April 2020, he has joined RWTH Aachen University. He is the head of the Chair  for Distributed Signal Processing and co-director of the Institute for Communication Technologies and  Embedded Systems. His professional interests are related to broad areas of digital signal processing and  artificial intelligence with applications in wireless communications. He is a fellow of IEEE and a  distinguished lecturer of IEEE Vehicular Technology Society with numerous scientific publications  (journals, conferences, and patents) and invited/tutorial talks. He is a recipient of several Nokia innovation  awards, IEICE Communications Society Best Paper Award in 2021, IEICE Communication System Study  Group Best Paper Award (joint 2014, 2015, 2017), The 2013 Alcatel-Lucent Award of Excellence, the 2012  KDDI Foundation Research Award, the 2009 KDDI Foundation Research Grant Award, the 2008 JSPS  Postdoctoral Fellowships for Foreign Researchers, the 2005 Active Research Award in Radio  Communications, 2005 Vehicular Technology Conference (VTC 2005-Fall) Student Paper Award from  IEEE VTS Japan Chapter and the 2004 Institute of IEICE Society Young Researcher Award. Furthermore,  he served as an Associate Editor of IEEE Communications Magazine, associate editor of IEICE  Transactions on Communications and IET Communications. In addition, he acted as a general chair and  technical program committee member of numerous IEEE conferences.

Abstract

With artificial intelligence (AI) machines can be designed to perform “autonomous” tasks (e.g. planning, problem solving) without being programmed to accomplish a single (repetitive) task, while being adaptive to different environments. We presume that AI provides techniques to enable autonomous operations of wireless systems with stringent service requirements in real time. Such system requires full awareness of its environment in real time while being designed not only through data-driven methodology by ML, but through knowledge management by AI. This talk discusses challenges and opportunities to embrace AI in the design of next-generation wireless communication systems. We start discussion by summarizing the properties of training-free and training-based methods of AI in wireless environment. To understand limitations of these methods we compare the traditional optimization theory, deep learning and reinforcement learning in wireless environment. Finally, we discuss the conceptual functions of autonomous agent with knowledge management. The talk provokes new coming challenges and unveil interesting future directions across multi-disciplinary research areas.

Fabien LOTTE: On Humans and Machines in Brain-Computer Interaction

Research Director

Inria

France

Short biography

Fabien LOTTE obtained a M.Sc., a M.Eng. and a PhD degree in computer sciences, all from the National Institute of Applied Sciences (INSA) Rennes, France, in 2005 (M.Sc., M.Eng.) and 2008 (PhD). His PhD Thesis received both the PhD Thesis award 2009 from AFRIF (French Association for Pattern Recognition) and the PhD Thesis award 2009 accessit (2nd prize) from ASTI (French Association for Information Sciences and Technologies). In 2009 and 2010, he was a research fellow at the Institute for Infocomm Research (I2R) in Singapore, working in the Brain-Computer Interface Laboratory. From January 2011 to September 2019, he was a Research Scientist (with tenure) at Inria Bordeaux Sud-Ouest, France, in team Potioc (http://team.inria.fr/potioc/). He obtained an Habilitation (HDR) in Computer Science from the University of Bordeaux in September 2016. Between October 2016 and January 2018, he spent 1-year as a visiting scientist at RIKEN Brain Science Institute, Japan, in Cichocki’s laboratory for advanced brain signal processing. Since October 2019, he is a Research Director (DR2) at Inria Bordeaux Sud-Ouest, France, still in team Potioc.

His research interests include Brain-Computer Interfaces (BCI), human-computer interaction, pattern recognition and brain signal processing. He is part of the editorial boards of the journals Brain-Computer Interfaces (since 2016), Journal of Neural Engineering (since 2016) and IEEE Transactions on Biomedical Engineering (since 2021). He is also co-specialty chief editor of Frontiers in Neuroergonomics: Neurotechnology and System Neuroergonomics. He co-edited the books “Brain-Computer Interfaces 1: foundations and methods” and “Brain-Computer Interfaces 2: technology and applications“, published both in French and in English in 2016, as well as the book “Brain-Computer Interfaces Handbook: Technological and Theoretical Advance” published in 2018 . In 2016, he was the recipient of an ERC Starting Grant (project BrainConquest) to develop his research on BCI.

Abstract

Brain-Computer Interfaces (BCIs) are systems that translate users’ brain activity, typically measured using ElectroEncephaloGraphy (EEG), into commands for an application. For instance, they can be used to move a computer cursor on screen towards the left or right by having users imagining left or right hand movements, recognized from EEG signals. They appear very promising for numerous applications, that I will touch upon in this talk, including assistive technologies for motor-impaired users, video games or neuroadaptive technologies, adapting the interaction content (e.g., exercice difficulty during a training task or number of information to monitor for plane pilots) to the users’ mental states (e.g., mental workload or fatigue). Despite such promises, BCIs are still scarcely used outside laboratories, mainly due to a lack of reliability. They indeed often recognize erroneous commands from the users, a subtantial proportion of users are unable to use them, and they have decoding performances that vary widely both between users but also within users (e.g., across days for the same user). Thus, one of the major challenges of the BCI research community is thus to make those BCIs realiable. In this talk, I argue that for doing so, we need to consider not only the machines (in particular the machine learning algorithms used to process EEG signals) but also the human users in the Brain-Computer Interaction loop. Ideally we should also consider the human-machine interactions when designing BCIs. I will illustrate these points by research works, both from our group and others, that first aim at understanding BCI limitations, e.g., at the human level by identifying human factors (e.g., users profile, mental states or neurophysiological patterns) influencing BCI performance and variabilities and at the machine level, by computationally modelling these factors influences and their interaction. Then, I will present some research works that aimed at addressing these limitations, notably at the machine level, by designing robust machine learning algorithms to process EEG signals – in particular based on Riemannian geometry classifiers, and at the human level, by designing personalized BCI user training approaches. I will conclude by providing some perspective about how both the human and the machine levels should be ideally jointly integrated and optimized for future reliable BCI designs.