Prof. Romeo Ortega
IEEE Fellow, Full Professor at ITAM, Mexico
Speech Title: Adaptive Observers for Mechanical and Electromechanical Systems
Abstract: In this talk we present a new approach to state observation, called Parameter Estimation-based Observers (PEBO) whose main idea is to translate the state estimation problem into one of estimation of constant, unknown parameters. The class of systems for which is applicable is identified via two assumptions related to the transformability of the system into a suitable cascaded form and our ability to estimate the unknown parameters. The first condition involves the solvability of a partial differential equation while the second one requires some persistency of excitation--like conditions. We present also PEBO in a unified framework together with the---by-now classical---Kasantzis-Kravaris-Luenberger and Immersion and Invariance observers. Morevoer, for systems for which a linear regression-like relation is available, it is possible to combine PEBO with a new estimation technique called Dynamic Regressor Extension and Mixing (DREM). This new technique, called DREMBAO, is used to generate adaptive observers. PEBO and DREMBAO are shown to be applicable to position estimation of a class of electromechanical systems---including motors and MagLev systems---and for speed observation of a class of mechanical systems.
The performance of these observers is compared with high-gain and sliding mode observers. As expected, it is shown that---in the presence of noise---the performance of the two latter designs is significantly below par with respect to the other techniques.
Brief Introduction to Prof. Romeo Ortega:
Romeo Ortega was born in Mexico. He obtained his BSc in Electrical and Mechanical Engineering from the National University of Mexico, Master of Engineering from Polytechnical Institute of
Leningrad, USSR, and the Docteur D`Etat from the Polytechnical Institute of Grenoble, France in 1974, 1978 and 1984 respectively.
He then joined the National University of Mexico, where he worked until 1989. He was a Visiting Professor at the University of Illinois in 1987-88 and at McGill University in 1991-1992, and a Fellow of the Japan Society for Promotion of Science in 1990-1991.
He was a member of the French National Research Council (CNRS) from June 1992 to July 2020, where he was a ``Directeur de Recherche" in the Laboratoire de Signaux et Systemes (CentraleSupelec) in Gif-sur-Yvette, France. Currently, he is a full time Professor at ITAM in Mexico. His research interests are in the fields of nonlinear and adaptive control, with special emphasis on applications.
Dr Ortega has published six books and more than 350 scientific papers in international journals, with an h-index of 85. He has supervised more than 35 PhD thesis. He is a Fellow Member of the IEEE since 1999 (Life 2020)and an IFAC Fellow since 2016. He has served as chairman in several IFAC and IEEE committees and participated in various editorial boards of international journals. He is currently Editor in Chief of Int. J. Adaptive Control and Signal Processing and Senior Editor of Asian Journal of Control.
Prof. Petros Ioannou
A.V. ‘BAL’ Chair Professor, University of Southern California
Speech Title: Traffic Flow Control in a Connected Environment
Abstract: Self- Driving cars are attracting a lot of attention and excitement as they will impact driving comfort and safety as well as modify the current modes of transporting people and goods. Getting rid of the driver however will not necessarily reduce congestion whose main cause is the high volume of vehicles competing in space and time to reach destinations. Connectivity however and compliance to traffic management commands and traffic rules by vehicle autopilots will open the way for far better traffic flow control approaches with strong potential to improve capacity, manage congestion and incidents in a much more effective way.
In this talk, we present several control techniques that can be used to control traffic in a connected environment where vehicles can communicate with the infrastructure. The control techniques involved combined lane change and variable speed control strategies which are analytically shown to effectively control congestion at bottlenecks. Microscopic simulations of traffic in a road network in Southern California near the ports of Los Angeles and Long beach are used to demonstrate the results and quantify the benefits of traffic control.
Brief Introduction to Prof. Petros Ioannou:
Petros A. Ioannou received a B.Sc. degree with First Class Honors from University College, London, England, in 1978 and the M.S. and Ph.D. degrees from the University of Illinois, Urbana, Illinois, in 1980 and 1982, respectively. In 1982, Dr. Ioannou joined the Department of Electrical Engineering-Systems, University of Southern California, Los Angeles, California. He is currently a Professor in the same Department and holds the A.V. ‘Bal’ Balakrishnan Chair. He is the Director of the Center of Advanced Transportation Technologies and Associate Director for Research of METRANS, a University Transportation Center. He also holds a courtesy appointment with the Department of Aerospace and Mechanical Engineering and the Department of Industrial Engineering. Dr. Ioannou was the recipient of the Outstanding Transactions Paper Award by the IEEE Control System Society in 1984 and the recipient of a 1985 Presidential Young Investigator Award for his research in Adaptive Control. In 2009 he received the IEEE ITSS Outstanding ITS Application Award and the IET Heaviside Medal for Achievement in Control by the Institution of Engineering and Technology (former IEE). In 2012 he received the IEEE ITSS Outstanding ITS Research Award and in 2015 the 2016 IEEE Transportation Technologies Award. Dr. Ioannou is a Fellow of IEEE, Fellow of International Federation of Automatic Control (IFAC), Fellow of the Institution of Engineering and Technology (IET), Fellow of AAAS and the author/co-author of 8 books and over 400 research papers in the area of controls, vehicle dynamics and control and intelligent transportation systems.
University of California, Merced
Title: Smart Control Engineering (SCE) Enabled by Digital Twin
Abstract: Experienced control engineers and researchers agree that before we design a controller we need to ask two questions 1) “What do we have/know?” and 2) “What do we want?” and after we have designed a controller, we also need to ask two questions 1) “How optimal?” and 2) “How robust?” Now, with the emerging wave of “Digital Transformation” such as Industry 4.0, I advocate to ask the third question: “How smart?” This talk suggests a new frontier for control engineering: SCE - Smart Control Engineering and I will show that digital twins (DT) are the enabler towards SCE, a consequence of IAI (industrial artificial intelligence). By “smartness”, following the notion of US NSF program on S&AS (smart and autonomous systems), we signify the following 5 attributes 1) Taskable; 2) Cognitive; 3) Reflective; 4) Ethical; 5) Knowledge-rich. In this talk, we will show a case study to illustrate the SCE enabled by DT using IAI.
A brief introduction to Prof.YangQuan Chen:
YangQuan Chen earned his Ph.D. from Nanyang Technological University, Singapore, in 1998. He had been a faculty of Electrical Engineering at Utah State University (USU) from 2000-12. He joined the School of Engineering, University of California, Merced (UCM) in summer 2012 teaching “Mechatronics”, “Engineering Service Learning” and “Unmanned Aerial Systems” for undergraduates; “Fractional Order Mechanics”, “Linear Multivariable Control”, “Nonlinear Controls” and “Advanced Controls: Optimality and Robustness” for graduates. His research interests include mechatronics for sustainability, cognitive process control (smart control engineering enabled by digital twins), small multi-UAV based cooperative multi-spectral “personal remote sensing”, applied fractional calculus in controls, modeling and complex signal processing; distributed measurement and control of distributed parameter systems with mobile actuator and sensor networks. He received Research of the Year awards from USU (2012) and UCM (2020). He was listed in Highly Cited Researchers by Clarivate Analytics in 2018, 2019 and 2020. His lab website is http://mechatronics.ucmerced.edu/ and his publication list is at https://scholar.google.com/citations?user=RDEIRbcAAAAJ&hl=en (Email: firstname.lastname@example.org)
Prof.Anthony G Cohn
University of Leeds, UK
A brief introduction to Prof.Anthony G Cohn:
Anthony Cohn is Professor of Automated Reasoning in the School of Computing, at the University of Leeds. His current research interests range from theoretical work on spatial calculi and spatial ontologies, to cognitive vision, grounding language to vision, robotics, modelling spatial information in the hippocampus, and Decision Support Systems, particularly for the built environment. He is Editor-in-Chief Spatial Cognition and Computation and was previously Editor-in-chief of the AI journal. He is the recipient of the 2015 IJCAI Donald E Walker Distinguished Service Award which honours senior scientists in AI for contributions and service to the field during their careers, as well as the 2012 AAAI Distinguished Service Award. He is a Fellow of the Royal Academy of Engineering, the Alan Turing Institute in the UK, and is also a Fellow of AAAI, AISB, EurAI (formerly ECCAI; Founding Fellow), the BCS, and the IET. He is a Distinguished Visiting Professor at Tongji University and Qingdao University of Science and Technology, an Adjunct Professor at Shandong University.
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Prof. Yang Shi
Department of Mechanical Engineering
University of Victoria, Victoria, Canada
Speech Title: A Robust Model Predictive Control Framework for Intelligent Mechatronic Systems
Abstract: 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 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.
Brief Introduction to Prof. Yang Shi:
Yang Shi received his B.Sc. and Ph.D. degrees in mechanical engineering and automatic control from Northwestern Polytechnical University, Xi’an, China, in 1994 and 1998, respectively, and 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, SK, 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, BC, 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, and the Faculty of Engineering Teaching Excellence Award in 2012 at the University of Victoria (UVic). He is the recipient of the JSPS Invitation Fellowship (short-term) in 2013, the UVic Craigdarroch Silver Medal for Excellence in Research in 2015, the 2017 IEEE Transactions on Fuzzy Systems Outstanding Paper Award, the Humboldt Research Fellowship for Experienced Researchers in 2018. He is a member of the IEEE IES Administrative Committee and the IES Fellow Evaluation Committee during 2017-2019; he is the Chair of IEEE IES Technical Committee on Industrial Cyber-Physical Systems. Currently, he is Co-EIC for IEEE Transactions on Industrial Electronics; he also serves as Associate Editor for Automatica, IEEE Trans. Control Systems Technology, IEEE/ASME Trans. Mechatronics, IEEE Trans. Cybernetics, etc.
He is a Fellow of Engineering Institute of Canada (EIC), IEEE, ASME, and CSME, and a registered Professional Engineer in British Columbia, Canada.
Distributed Control & Autonomous Systems Lab. (DCASL)
School of Mechanical Engineering
Gwangju Institute of Science and Technology (GIST), Korea
Speech Title: Distributed Optimal Traffic Control: Modeling and Synthesis
Abstract: Motivated by the fact that intelligent traffic control systems have become inevitable, and to cope with the risk of traffic congestion in urban areas, a novel distributed control strategy for urban traffic networks is developed. Since these networks contain a large number of roads having different directions, each of them can be described as a multi-agent system. Thus, a coordination among traffic flows is required to optimize the operation of the overall network. In order to determine control decisions, we describe the objective of improving traffic conditions as a constrained optimization problem with respect to downstream traffic flows. By applying the gradient projection method and the minimal polynomial of a matrix pair, we propose algorithms that allow each road cell to determine its control decision corresponding to the optimal solution while using only its local information.
Brief Introduction to Prof.Hyo-Sung Ahn:
Hyo-Sung Ahn received the B.S. and M.S. degrees in astronomy from Yonsei University, Seoul, Korea, in 1998 and 2000, respectively, the M.S. degree in electrical engineering from the University of North Dakota, Grand Forks, in 2003, and the Ph.D. degree in electrical engineering from Utah State University, Logan, UT, USA, in 2006. He is currently a Professor at the School of Mechanical Engineering, Gwangju Institute of Science and Technology (GIST), Gwangju, South Korea. Since July 2007, he has been with the School of Mechatronics and School of Mechanical Engineering, GIST. He was Dasan Distinguished Professor (Dasan Professor) from 2013 to 2018. Before joining GIST, he was a Senior Researcher at the Electronics and Telecommunications Research Institute, Daejeon, South Korea. He was a visiting scholar at Colorado School of Mines in 2019. His research interests include distributed control, aerospace navigation and control, network localization, and learning control. He is the author of the books “Iterative learning control: Robustness and Monotonic Convergence for Interval Systems,” Springer, 2007, and “Formation Control – Approaches for Distributed Agents,” Springer, 2020.
Prof. Ioannis Pitas
Department of Informatics
University of Thessaloniki
Speech Title: Drone vision for infrastructure inspection and maintenance
Abstract: Infrastructure inspection is one of the most important drone mission tasks. In most cases, long-range and/or local very accurate inspection of the infrastructure is needed, e.g., for bridges, road infrastructure or electrical installations. Various inspection modes are overviewed, e.g., visual inspection, thermography and 3D imaging (LIDAR), focusing on faulty object detection. Furthermore, infrastructure maintenance activities based on aerial manipulation involving force interactions are presented. Finally, aerial co-working safely and efficiently helping human workers in inspection and maintenance is overviewed. To this end, human-centered computing, e.g., human pose estimation, action recognition are needed.
Brief Introduction to Prof. Ioannis Pitas:
Prof. Ioannis Pitas (IEEE fellow, IEEE Distinguished Lecturer, EURASIP fellow) received the Diploma and PhD degree in Electrical Engineering, both from the Aristotle University of Thessaloniki (AUTH), Greece. Since 1994, he has been a Professor at the Department of Informatics of AUTH and Director of the Artificial Intelligence and Information Analysis (AIIA) lab. He served as a Visiting Professor at several Universities.
His current interests are in the areas of computer vision, machine learning, autonomous systems, intelligent digital media, image/video processing, human-centred interfaces, affective computing, 3D imaging and biomedical imaging. He has published over 1000 papers, contributed in 47 books in his areas of interest and edited or (co-)authored another 11 books. He has also been member of the program committee of many scientific conferences and workshops. In the past he served as Associate Editor or co-Editor of 9 international journals and General or Technical Chair of 4 international conferences. He participated in 70 R&D projects, primarily funded by the European Union and is/was principal investigator/researcher in 42 such projects. He has 31350+ citations to his work and h-index 84+ (Google Scholar).
Prof. Pitas lead the big European H2020 R&D project MULTIDRONE: https://multidrone.eu/. He is AUTH principal investigator in H2020 R&D projects Aerial Core and AI4Media. He is chair of the Autonomous Systems Initiative https://ieeeasi.signalprocessingsociety.org/. He is head of the EC funded AI doctoral school of Horizon2020 EU funded R&D project AI4Media (1 of the 4 in Europe)