ECC21 will feature the following plenary speakers:
The organisers have decided that the ECC 2021 will be a fully online conference. We regret that due to the Covid-19 situation we have been forced to change the planned hybrid meeting into a full online event.
ECC21 will feature the following seven (semi-) plenary lectures.
14:00-15:00 (CET time)
Machine learning is a set of techniques to extract mathematical models from data that has recently become extremely popular and very successful in many fields, including control. In my talk, I will present several approaches in which machine learning can help design and calibrate model predictive control (MPC) laws and simplify the associated on-line computations. I will focus on techniques for learning prediction models tailored to nonlinear and hybrid MPC, global and preference-based optimization methods using surrogate functions for actively learning optimal MPC parameters from calibration experiments, and unsupervised and supervised learning techniques for reducing the number of optimization variables in MPC.
Alberto Bemporad received his Master’s degree in Electrical Engineering in 1993 and his Ph.D. in Control Engineering in 1997 from the University of Florence, Italy. In 1996/97 he was with the Center for Robotics and Automation, Department of Systems Science & Mathematics, Washington University, St. Louis. In 1997-1999 he held a postdoctoral position at the Automatic Control Laboratory, ETH Zurich, Switzerland, where he collaborated as a senior researcher until 2002. In 1999-2009 he was with the Department of Information Engineering of the University of Siena, Italy, becoming an Associate Professor in 2005. In 2010-2011 he was with the Department of Mechanical and Structural Engineering of the University of Trento, Italy. Since 2011 he is Full Professor at the IMT School for Advanced Studies Lucca, Italy, where he served as the Director of the institute in 2012-2015. He spent visiting periods at Stanford University, University of Michigan, and Zhejiang University. In 2011 he cofounded ODYS S.r.l., a company specialized in developing model predictive control systems for industrial production. He has published more than 350 papers in the areas of model predictive control, hybrid systems, optimization, automotive control, and is the co-inventor of 16 patents. He is author or coauthor of various software packages for model predictive control design and implementation, including the Model Predictive Control Toolbox (The Mathworks, Inc.) and the Hybrid Toolbox for MATLAB. He was an Associate Editor of the IEEE Transactions on Automatic Control during 2001-2004 and Chair of the Technical Committee on Hybrid Systems of the IEEE Control Systems Society in 2002-2010. He received the IFAC High-Impact Paper Award for the 2011-14 triennial and the IEEE CSS Transition to Practice Award in 2019. He is an IEEE Fellow since 2010.
Professor of Control Systems
IMT School for Advanced Studies Lucca
09:30-10:30 slot (CET time)
Statistical filtering is a desirable mathematical framework for the estimation of the internal state variables of a dynamical system in the light of various sensors’ measurements. Since the advent of the space age, it has played a pivotal role in guidance and navigation problems in aeronautics and robotics. Its workhorse, the Kalman filter – albeit usable in a nonlinear context by linearizing about the estimated state trajectory – deeply builds upon the specificity of linear systems. As a result, key theoretical properties are lost in nonlinear contexts, in particular when dealing with challenging nonlinear problems related to the navigation of autonomous systems. However, it turns out that many such estimation problems bear a structure akin to linear systems, after a proper embedding of the state space into a matrix group has been found. Essentially, by replacing vector addition with matrix multiplication, linear observer design (or linear filter design) carries over, as well as a number of convergence and consistency guarantees discovered over the past fifteen years. We illustrate this perspective by addressing various problems, from the design of high performance industrial inertial navigation systems to robot simultaneous localization and mapping. For the latter, the geometric approach resolves problems connected to the notion of observability that have long impeded the use of the classical extended Kalman filter.
Silvère Bonnabel is Professor at University of New Caledonia and Mines ParisTech, France. He received the engineering (M. Sc) degree in applied mathematics and the Ph.D. degree in mathematics and control from Mines ParisTech in 2004 and 2007, respectively, and the Habilitation in mathematics from Sorbonne University in 2014. He was a postdoctoral fellow at the University of Liège, Belgium, in 2008, and was hired as a permanent faculty member at Mines ParisTech in 2009. In 2017, he was an Invited Fellow of Sidney Sussex College at the University of Cambridge, UK. His primary research interests revolve around systems and control, robotics, with a focus on state estimation and geometric methods. He has contributed to developing products in the aeronautics industry. He serves as an Associate Editor for IEEE Control Systems Magazine, and he has also served as an Associate Editor for Systems & Control Letters. He is a recipient of various awards including the joint IEEE & SEE Glavieux Award in 2015 and the Automatica Paper Prize in 2020.
University of New Caledonia and Mines ParisTech
14:00-15:00 slot (CET time)
In line with Moore’s law, the ever increasing demands on specifications of wafer scanners used in the production of microchips have pushed the performance of the control designs of such scanners to the limit. As such, inherent design limitations in linear feedback control have motivated scientists and engineers to explore nonlinear feedback strategies. An example is given by the recent developments in hybrid integrator-gain systems, abbreviated with HIGS. HIGS, which can be formally described in the framework of (extended) projected dynamical systems, operate alternately in so-called integrator mode and in gain mode, and have properties and associated (phase) benefits similar to reset control systems. However, HIGS do not produce discontinuous control signals due to the absence of (partial) state resets. This is considered useful in reducing higher harmonics, which are generally induced by nonlinear feedback control, and, which potentially excite structural dynamics of the wafer scanner and as a result may compromise closed-loop performance. HIGS, and in particular HIGS-PID control, offer the possibility to outperform any linear feedback in terms of transient performance by inducing little or no overshoot. Additionally, in terms of steady-state performance, HIGS-PID control can benefit from designing unstable linear modes within the hybrid structure, which leads to increased bandwidths, improved low-frequency disturbance suppression, and better high-frequency noise attenuation. The price to pay is more complexity in the control design and the stability and performance analysis, which may contradict the intuition of less is more. Toward meeting future specifications of wafer scanners, however, the increased design freedom with HIGS-PID control, if used to exceed beyond existing boundaries, is demonstrated to legitimize such complexity, hence Moore is less. In the semi-plenary lecture, an industrial perspective on HIGS-PID control will be given that highlights inherent design limitations, frequency-domain stability tools, robust nonlinear control design, and nonlinear stage performance of wafer scanners.
Marcel Heertjes received both his MSc and PhD in mechanical engineering from Eindhoven University of Technology in 1995 and 1999, respectively. In 2000, he joined the Philips Centre for Industrial Technology in Eindhoven. In 2007 he joined ASML as principle engineer and control competence leader. Marcel Heertjes is also part-time professor on nonlinear industrial control for high-precision mechatronics at the department of mechanical engineering at Eindhoven University of Technology.
His main research interests are with industrial control for high-precision mechatronic systems with special focus on nonlinear control, feedforward and learning control, and data-driven optimization and self-tuning. His nonlinear control contributions and developments focus on full support of frequency-domain design, synthesis, loopshaping, and qualification tools that attempt at breaking free from linear control design limitations. His primary ambition is to provide a clear view on (a) robust nonlinear control design and stability analysis, and (b) the associated data-driven controller synthesis and tuning.
He served as guest editor of International Journal of Robust and Nonlinear Control (2011) and IFAC Mechatronics (2014) and currently is associate editor of IFAC Mechatronics (since 2016). In 2015, he received the IEEE Control Systems Technology Award ‘‘For the development and application of variable-gain control techniques for high-performance motion systems’’.
09:30-10:30 slot (CET time)
All day long, our fingers touch, grasp and move objects in various media such as air, water, oil. We do this almost effortlessly – it feels like we do not spend time planning and reflecting over what our hands and fingers do or how the continuous integration of various sensory modalities such as vision, touch, proprioception, hearing help us to outperform any other biological system in the variety of the interaction tasks that we can execute. Largely overlooked, and perhaps most fascinating is the ease with which we perform these interactions resulting in a belief that these are also easy to accomplish in artificial systems such as robots.
When interacting with objects, the robot needs to consider geometric, topological, and physical properties of objects. This can be done either explicitly, by modeling and representing these properties, or implicitly, by learning them from data. The main scientific objective of this project is to create new informative and compact representations of deformable objects that incorporate both analytical and learning-based approaches and encode geometric, topological, and physical information about the robot, the object, and the environment. We will do this in the context of challenging multimodal, bimanual object interaction tasks. The focus in our work is on physical interaction with deformable objects using multimodal feedback, generative models and address stability in contact rich tasks.
Danica Kragic is a Professor at the School of Computer Science and Communication at the Royal Institute of Technology, KTH. She received MSc in Mechanical Engineering from the Technical University of Rijeka, Croatia in 1995 and PhD in Computer Science from KTH in 2001. She has been a visiting researcher at Columbia University, Johns Hopkins University and INRIA Rennes. She is the Director of the Centre for Autonomous Systems. Danica received the 2007 IEEE Robotics and Automation Society Early Academic Career Award. She is a member of the Royal Swedish Academy of Sciences, Royal Swedish Academy of Engineering Sciences and Young Academy of Sweden. She holds a Honorary Doctorate from the Lappeenranta University of Technology. She chaired IEEE RAS Technical Committee on Computer and Robot Vision and served as an IEEE RAS AdCom member. Her research is in the area of robotics, computer vision and machine learning. She received ERC Starting and Advanced Grants. Her research is supported by the EU, Knut and Alice Wallenberg Foundation, Swedish Foundation for Strategic Research and Swedish Research Council. She is an IEEE Fellow.
School of Electrical Engineering
and Computer Science
Royal Institute of Technology, KTH
09:30-10:30 (CET time)
Extremum seeking (ES) control is a data-driven approach for optimization of the steady-state behaviour of nonlinear dynamical systems. This methodology is especially useful when the model and/or cost function is not available for design. Because of its model-free and data-based nature extremum seeking has a broad range of applications, from multi-agent systems, power systems, telecommunications, biochemical processes, automotive and transport systems to financial and biological systems. As an example, ES can be exploited to enable better energy capture by wind turbines.
There exists a rich literature on extremum seeking with a range of algorithms and approaches that can be classified in different ways, such as global/local optimization, unconstrained/constrained problems, stochastic/deterministic methods and so on. Common to all these methods is that they combine data-driven learning (i.e. estimation) and optimization in various ways to achieve their goal. The focus of the talk will be on these common underlying concepts and intuition whereby imperfect data-driven estimates are combined with robust optimization schemes to achieve extremum seeking. This naturally leads to a unifying design methodology whereby different learning algorithms can be combined with different optimizers to generate extremum seeking algorithms. The strength and potential of the methodology will be illustrated by several real-world examples that I studied with my students and collaborators.
Dragan Nesic is a Professor at the Department of Electrical and Electronic Engineering at The University of Melbourne. He received his Bachelor of Mechanical Engineering Degree at the University of Belgrade (1990) and his PhD at the Australian National University (1997). Professor Nesic’s research interests are in the broad area of control engineering including its mathematical foundations (e.g. Lyapunov stability theory, hybrid systems, singular perturbations, averaging) and its applications to various areas of engineering (e.g. automotive control, optical telecommunications) and science (e.g. neuroscience). More specifically, he has made significant contributions to the areas of nonlinear sampled-data systems, nonlinear networked control systems, event-triggered control, optimization-based control and extremum seeking control and he presented several keynote lectures on these topics at international conferences.
Prof. Nesic is a Fellow of IEEE and a Fellow of IFAC and he served as a Distinguished Lecturer of the Control Systems Society of the IEEE. He was a co-recipient (with M. Nagahara and D. Quevedo) of the George S. Axelby Outstanding Paper Award (2017). He is a recipient of numerous awards and prizes, including Doctorate Honoris Causa by the University of Lorraine (2019), Humboldt Research Award (2020), Humboldt Research Fellowship (2003-2004), as well as Future Fellowship (2010-2014) and an Australian Professorial Fellowship (2004-2009) funded by the Australian Research Council. He is an Associate Editor for the journal IEEE Transactions on Network Control Systems (CONES) and Foundations and Trends in Systems and Control. He has also served as Associate Editor for the IEEE Transactions on Automatic Control , Automatica , European Journal of Control and Systems and Control Letters . Prof. Nesic was a General Co-Chair of 2017 IEEE Conference on Decision and Control and a General Chair of the 2011 Australian Control Conference. He served on International Program Committees of many international conferences, such as the American Control Conference, IEEE Conference on Decision and Control, NOLCOS, Asian Control Conference, European Control Conference, and so on. Prof. Nesic also served on various committees including the Board of Governors, IEEE Control Systems Society.
Electrical and Electronic Engineering
The University of Melbourne
14:00-15:00 slot (CET time)
A digital twin is a set of coupled computational models that evolves over time to persistently represent the structure, behavior, and context of an individual physical asset such as a component, system, or process. Digital twins have the potential to bring value to decision-making in a broad range of societal, natural, and engineering systems. This talk presents a probabilistic graphical model as a formal mathematical representation of a digital twin and its associated physical asset. We create an abstraction of the asset-twin system as a set of coupled dynamical systems, evolving over time through their respective state-spaces and interacting via observed data and control inputs. The abstraction is realized computationally as a dynamic decision network. Predictive capabilities are enabled by physics-based reduced-order models. We demonstrate how the approach is instantiated to create, update and deploy a structural digital twin of an unmanned aerial vehicle.
Karen E. Willcox is Director of the Oden Institute for Computational Engineering and Sciences and a Professor of Aerospace Engineering and Engineering Mechanics, at the University of Texas at Austin. She holds the W. A. “Tex” Moncrief, Jr. Chair in Simulation-Based Engineering and Sciences and the Peter O’Donnell, Jr. Centennial Chair in Computing Systems. Prior to joining the Oden Institute in 2018, she spent 17 years as a professor at the Massachusetts Institute of Technology, where she served as Professor of Aeronautics and Astronautics, the founding Co-Director of the MIT Center for Computational Engineering, and the Associate Head of the MIT Department of Aeronautics and Astronautics. She is also an External Professor at the Santa Fe Institute.
Willcox holds a Bachelor of Engineering Degree from the University of Auckland, New Zealand, and masters and PhD degrees from MIT. Prior to becoming a professor at MIT, she worked at Boeing Phantom Works with the Blended-Wing-Body aircraft design group.
Willcox is Fellow of the Society for Industrial and Applied Mathematics (SIAM) and Fellow of the American Institute of Aeronautics and Astronautics (AIAA). She has served in multiple leadership positions within AIAA and SIAM, including leadership roles in the SIAM Activity Group on Computational Science and Engineering and in the AIAA Multidisciplinary Design Optimization Technical Committee. She is a current member of the AIAA Board of Trustees. She is a current member of the National Academies Board on Mathematical Sciences and Analytics, and has served on five National Academies studies and review panels. In 2017, she was awarded Member of the New Zealand Order of Merit (MNZM).
Oden Institute for Computational
Engineering & Sciences
The University of Texas at Austin
09:30-10:30 slot (CET time)
Wind energy is expected to be the largest European source of energy by 2030 and therefore largely responsible for enabling Europe to achieve its goal of having at least 27% of its electrical energy generated by renewable sources. In existing and new wind farms the wind turbines still operate on an individual level, therefore each wind turbine optimizes its own power production, resulting in sub-optimal power output at wind farm level. Previously, many researchers showed that yaw control, which is an already existing control degree of freedom, can help minimize the interaction between different turbines for existing wind farms under quasi-steady conditions. However, for realistic inflow conditions a challenging time-varying control problem has to be solved. For this emerging field a dynamic control solution is still lacking. In this presentation, I will present recent results and challenges for the field of dynamic wind farm control
Jan-Willem van Wingerden graduated cum laude in mechanical engineering at TU Delft in 2004. In 2008 he obtained his doctorate, cum laude, in mechanical engineering at the Delft Center for Systems and Control (DCSC) for his study Control of Wind Turbines with ‘Smart’ Rotors: Proof of Concept & LPV Subspace. After obtaining his doctorate, Wingerden started working as a tenure tracker at DCSC (3mE). In 2012 he was awarded an NWO Veni grant and in 2019 an NWO VIDI grant both in the field of wind farm control. Since 2017 he is leading the data-driven control section within the Delft Center for Systems and Control, where he is currently a Full Professor.
Jan-Willem is passionate about the development of data-driven control systems for wind turbines and wind farms. In recent years he and his team developed novel data-driven flow models for coordinated wind farm control and showed that these models are key enablers for robust closed-loop wind farm control.
Delft Centre for Systems and Control
Delft University of Technology