Dong-il “Dan” Cho, Seoul National University, South Korea
Biomimetic Robots: Current Status and Future Directions
Abstract: Nature has been an inspiration for many technological innovations. Especially, research in biomimetic robots has been motivated by the superior characteristics of animals and insects which are utilized to overcome the limitations of conventional technologies. These biomimetic robots are being developed by applying various bio-electro-mechanical analyses, new materials, advanced nanotechnologies, and integration of various functionalities. In the future, autonomous biomimetic robots will also be developed by combining high-level artificial intelligence and ultra-low power circuits and wireless communication technologies.
In this talk, the state-of-the-art of biomimetic robots is presented. Representative ground biomimetic robots, aerial biomimetic robots, and underwater biomimetic robots will be introduced. Furthermore, the technological limitations of the present biomimetic robots and the direction of development that needs to be taken to overcome them will be discussed.
Speaker’s profile: Dong-il “Dan” Cho is a Professor in the Department of Electrical and Computer Engineering at Seoul National University in Korea. He is also the director of Bio-Mimetic Robot Research (BMRR) Center at Seoul National University. He is the author/coauthor of more than 130 international journal articles and the holder/coholder of more than 110 US and Korean patents. He has served on the editorial board of many international journals. Currently, he is Senior Editor of IEEE Journal of MEMS and IFAC’s Mechatronics. He served as the President of ICROS (IFAC NMO), Technical Board Member of IFAC, Council Member of IFAC, and BOG Member of IEEE CSS. He is currently Vice President of IFAC (and Chair of the Technical Board). He received the IFAC Outstanding Service Award (2011) and the ICROS Award (2015). He also received awards from the Minister of Information and Communication of Korea (2006) and the Prime Minster of Korea (2018). He was elected to Ordinary Member (2010-2012) and Senior Member (2013-2017, 2018- ) of National Academy of Engineering of Korea.
P.M.J. Van den Hof, Eindhoven University, Netherlands
Data-driven model learning in linear dynamic network
Abstract: Interconnected networks of dynamic systems play a growing role in science and technology, leading to decentralized, distributed and multi-agent type of control problems. From the modeling side, this leads to an urge to develop data-driven methods for learning models in/for large-scale interconnected systems. In this plenary the main developments and challenges in this area will be highlighted for the situation of linear dynamic networks with continuous dynamics. Besides setting up a modelling framework for directed networks, we will address problems of network identifiability and of local identification of a particular part of the network, including the selection of the appropriate signals to be measured. Machine learning tools are incorporated for mitigating the model structure selection problems and graph-based methods are introduced for verifying identifiability, providing conditions for the location of excitation signals in the network graph.
Speaker’s Profile: Paul M. J. Van den Hof received the M.Sc. and Ph.D. degrees in electrical engineering from Eindhoven University of Technology, Eindhoven, The Netherlands. In 1986 he moved to Delft University of Technology, where he was appointed as Full Professor in 1999. From 2003 to 2011, he was founding co-director of the Delft Center for Systems and Control (DCSC). From 2005-2015 he was scientific director of the Dutch Institute for Systems and Control (DISC). As of 2011, he is a Full Professor and heading the Control Systems Group in the Electrical Engineering Department of Eindhoven University of Technology. His research interests include system identification, identification for control, and model-based control and optimization, with applications in industrial process control systems, and high-tech systems. He holds an ERC Advanced Research grant for a research project on identification in dynamic networks. Paul Van den Hof is an IFAC Fellow and IEEE Fellow, and Honorary Member of the Hungarian Academy of Sciences. He has been a member of the IFAC Council (1999–2005, 2017-2020), the Board of Governors of IEEE Control Systems Society (2003–2005), and an Associate Editor and Editor of Automatica (1992–2005). In the triennium 2017-2020 he serves as chair of the Executive Board and Vice-President of IFAC.
Prof. Babatunde Ogunnaike, University of Delaware, USA
Biological Control Systems: The Future of Engineering In Medicine
Abstract: The mammalian organism maintains stable, efficient and “near-optimal” performance and homeostasis in the face of external and internal perturbations via distinct biological systems ranging from the large-scale physiological (nervous, endocrine, immune, circulatory, respiratory, etc.), to the cellular (growth and proliferation regulation, DNA damage repair, etc.), and the sub-cellular (gene expression, protein synthesis, metabolite regulation, etc). “Biological Control Systems,” a sub-topic of Control Theory, arises from a control engineering perspective of the function, organization, and coordination of these multi-scale biological systems and the control mechanisms that enable them to carry out their functions effectively. In this presentation, we will provide an overview of how physiological life is made possible by control; demonstrate the usefulness of a control engineering perspective of pathologies for diagnosis, design, and implementation of effective treatments—especially for precision (personalized) medicine; and hence make the case for the central role engineering will play in enabling medicine of the future. The concepts and principles will be illustrated using a specific clinical example involving platelet count control for an immune thrombocytopenic purpura (ITP) patient.
Speaker’s Profile: Babatunde A. Ogunnaike is the William L. Friend Chaired Professor of chemical engineering and, until October 1, 2019, dean of the College of Engineering at the University of Delaware. He received the B.Sc. degree in Chemical Engineering from the University of Lagos, Nigeria; the M.S. degree, in Statistics and the Ph.D. degree in Chemical Engineering both from the University of Wisconsin–Madison. He is the author or co-author of four books including a widely used textbook, Process Dynamics, Modeling and Control, and Random Phenomena: Fundamentals of Probability and Statistics for Engineers. His awards include the American Institute of Chemical Engineers 1998 CAST Computing Practice Award, the 2007 ISA Eckman Award, and the 2008 AACC Control Engineering Practice award. He was named a fellow of the American Institute of Chemical Engineers in 2009, a fellow of the American Association for the Advancement of Science in 2015; he was also elected to fellowship of the Nigerian Academy of Engineering and elected to the US National Academy of Engineering both in 2012.
Prof. Srinivasan Parthasarathy, Ohio State University, USA
Stochastic Flow Clustering: Consolidation, Renewed Bearing and Applications
Speaker’s profile: Srinivasan Parthasarathy is a Professor of Computer Science and Engineering and the director of the data mining research laboratory at Ohio State. His research interests span data analytics, databases and high-performance computing. He is among a handful of researchers nationwide to have won both the Department of Energy and National Science Foundation Career awards. He and his students have won multiple best paper awards or “best of” nominations from leading forums in the field including: SIAM Data Mining, ACM SIGKDD, VLDB, ISMB, WWW, ICDM, and ACM Bioinformatics. He chaired the SIAM data mining conference steering committee (elected) till 2019, and serves or has served on the board of several journals in parallel computing, machine learning and data mining. Since 2012 he also helped lead the creation of OSU’s first-of-a-kind nationwide (US) undergraduate major in data analytics and serves as one of its founding directors.
Prof. Shankar Narasimhan, IIT Madras, India
Extracting interpretable information from data through behavioural model identification
Abstract: The lack of interpretability of the results/predictions from the application of data analytic and machine learning techniques is now recognized as a major stumbling block for wide use of such techniques, especially in engineered systems. This has led to significant activity among artificial intelligence (AI) researchers in development of interpretable models from data.
In classical approaches to modelling of engineered systems, the behaviour of the system is modelled using conservation laws (material, energy, momentum), rate laws, thermodynamic constraints and property correlations. A key feature of this modelling exercise is that it is independent of the intended application (design, estimation, fault diagnosis, control, or optimization). In principle, the same model can be used for different applications. Following a similar approach to data driven modelling, we propose to ‘learn’ or ‘identify’ the behaviour model of the system, without recourse to the intended application. Specifically, for systems satisfying linear conservation laws (eg. flow balances), we show how Principal Components Analysis (PCA) can be used to derive the linear conservation equations relating the variables from noisy data. Under some additional mild conditions, we show how an iterative PCA technique can be used to also estimate the noise variances that corrupt the measured data. We demonstrate that this identified behavioural model can be used in much the same manner as that derived from first principles for a variety of applications such as optimal design of sensor networks, estimation, and fault diagnosis. The results obtained can also be explained based on the behavioural model and the criteria used in the application techniques.
Prof. Sebastian Engell, TU Dortmund, Germany
Robust Performance Optimizing NMPC by Multistage Optimization
Abstract: Multi-stage (N)MPC is a robust MPC formulation which includes the future feedback actions into the open-loop optimization, by considering recourse actions. It is formulated on a tree of possible future evolutions of the controlled system. For this discrete set of uncertainties, the multi-stage formulation provides the optimal closed-loop performance by solving an open-loop optimization problem. Multistage NMPC has been demonstrated to lead to efficient robust control for many examples.
Multi-stage MPC has been extended to output feedback based on state estimators. The inclusion of the estimation error significantly enlarges the scenario tree which leads to a rapid growth of the computational effort. Tube-enhanced multi-stage MPC combines the handling of large uncertainties by multi-stage MPC with the regulation against small disturbances and estimation errors using an ancillary controller and thus reduces the computational effort.
To reduce the conservatism, multi-stage MPC can be combined with the estimation of model-error models for non-parametric model mismatch and with parameter estimation for parametric mismatch. This can be extended to dual control. The multi-stage setting leads to an integrated formulation where the reduction of the parameter uncertainty influences the performance via its effect on the scenario tree.
Speaker’s profile: Sebastian Engell received a Dipl.-Ing degree in Electrical Engineering from Ruhr- Universität Bochum, Germany in 1978 and the Dr.-Ing. Degree and the venia legendi in Automatic Control from Universität Duisburg in 1981 and 1987. 1984/1985 he spent a year as a PostDoc at McGill University, Montréal, P.Q. 1986-1990 he was the head of an R&D group at the Fraunhofer Institut IITB in Karlsruhe, Germany. In 1990 he was appointed to his present position as a Full Professor of Process Dynamics and Operations in the Department of Chemical Engineering at TU Dortmund where he has graduated more than 70 Dr.-Ing. candidates.
Dr. Engell received an IFAC Journal of Process Control Best Paper Award in 2008, a Best Paper Award of the IEEE Congress on Evolutionary Computation 2010 with Thomas Tometzki and the PSE Model-based Innovation Prize with Ala Eldin Bouaswaig. He was a co-author of the 2014 Best Paper in Computers and Chemical Engineering, and with Weihua Gao and Simon Wenzel he received the 2016 Computers and Chemical Engineering Best Paper Award for the paper “A reliable modifier-adaptation strategy for real-time optimization”. He gave the Bayer Lecture in Process Systems Engineering at Carnegie Mellon University in 2008 and the Roger Sargent Lecture at Imperial College, London, in 2012. He is author or co-author of more than 130 Papers in scientific journals, more than 40 papers in edited volumes, and more than 350 conference papers with peer review and full papers in proceedings. In 2012, he was awarded a European Advanced Investigator Grant for the Project MOBOCON – Model-based Optimizing Control: from a Vision to Industrial Reality. Dr. Engell served as the President of EUCA, the European Control Association 2015-2017, as Vice-Rector of TU Dortmund 2002-2006, and twice as Department Chair. He is a Fellow of IFAC, the International Federation of Automatic Control, since 2006 and chaired the IFAC Fellow Selection Committee 2011-2014.