Plenary and Semi-Plenary Speakers

On data-driven control
(plenary talk “WeP_Audimax.1”, Wednesday 9:00, Audimax)

Abstract. We present a technique to design controllers from data for systems whose model is imprecisely known. The technique is based on collecting measurements of low complexity from the systems and using them for the synthesis of controllers, which is reduced to the solution of data-dependent semidefinite programs. The method provides stability certificates in the presence of perturbations on the dataset.

Short biography. Claudio De Persis is a professor with the Engineering and Technology Institute (ENTEG), Faculty of Science and Engineering, University of Groningen, the Netherlands, since 2011. He is also affiliated with the Jan Willems Center for Systems and Control. He received the Laurea and Ph.D. degrees in engineering in 1996 and 2000 both from the University of Rome "La Sapienza", Rome, Italy, and held positions at the University of Twente, the University of Rome "La Sapienza", Washington University, St. Louis, MO, USA, and Yale University, New Haven, CT, USA.

He was an Editor of the International Journal of Robust and Nonlinear Control (2006-2013), an Associate Editor of the IEEE Transactions on Control Systems Technology (2010-2015), the IEEE Transactions on Automatic Control (2012-2015), Automatica (2013-2021) and the IEEE Control Systems Letters (2017-2021).

His main research interest is in automatic control, and his recent research focuses on data-driven control and learning for control.

Some results on hierarchical control for parabolic equations
(plenary talk “ThP_Audimax.1”, Thursday 9:00, Audimax)

Abstract. In classical control theory, we usually have a state equation or system and just one control, with the mission of achieving a predetermined goal. Sometimes, the goal is to minimize a cost function in a prescribed family of admissible controls; this is the optimal control viewpoint. A more interesting situation arises when several (in general, conflictive or contradictory) objectives are considered. This may happen, for example, if the cost function is the sum of several terms and it is not clear how to average. It can also be expectable to have more than one control acting on the equation.

In this talk, we present an overview of the known results on this subject for the heat equation. We will recall the results of Araruna and collaborators where hierarchic exact controllability results were established for linear and semilinear heat equations. In this research, and in the seminal papers by J.-L. Lions, the main idea is to work with one primary control (the leader) and one or several secondary controls (the followers). For each possible leader, the associated followers try to minimize a functional (or reach equilibrium if there is more than one cost objective function). Then, the leader is chosen such that the associated state satisfies a final time constraint. We will present the recent result with E. Fernández-Cara et al., where we accomplish optimal control and controllability tasks with a hierarchy of controls. This time, however, the controllability goal will be commended to the follower, while the choice of the leader will be subject to an optimal control problem. It will be seen that this makes the problem more difficult to handle (essentially because we must work all the time in a very restrictive class of leader controls).

Short biography. Luz de Teresa did her undergraduate studies in mathematics at the Faculty of Sciences of the UNAM (National Autonomous University of Mexico). She obtained her professional degree in April 1990. With a grant from the DGAPA-UNAM, she received a Ph.D. in Applied Mathematics at the Complutense University of Madrid. She entered as a researcher at the Institute of Mathematics of the UNAM in April 1995, where she continues to work. She collaborates with mathematicians and engineers from Mexico, France, Spain, Chile, Brazil, the United States, Italy, Romania and Argentina.

So far she has 49 research articles published in highly recognized journals. These works have received more than 700 citations. She also works in the field of teaching and training of young researchers and students, as well as in terms of the design and development of national and international research projects, and the organization of numerous relevant academic events. She is currently a member of the UNAM Governing Board and was president of the Mexican Mathematical Society (SMM) for the 2018-2020 period. Finally, Dr. de Teresa is a member of various editorial committees in international journals.

The central theme of Prof. de Teresa's works is the control of coupled parabolic equations and related problems.

Deep Learning and Optimal Control
(plenary talk “TuP_Audimax.1”, Tuesday 9:00, Audimax)(plenary talk)

Abstract. There is a close analogy between deep learning and optimal control. This analogy can be exploited to develop deep learning-based algorithms for optimal control, and optimal control-based algorithms for deep learning. I will discuss the progress made along these directions.

Short biography. Weinan E is a professor at Peking University and in the Department of Mathematics and Program in Applied and Computational Mathematics at Princeton University. He got his Ph.D. from UCLA in 1989 and afterwards held visiting positions at NYU and the Institute for Advanced Study. He was on the faculty of the Courant Institute from 1994 to 1999 before moving to Princeton.

Weinan E was awarded the ICIAM Collatz Prize in 2003, the SIAM Kleinman Prize in 2009 and the SIAM von Karman Prize in 2014, the SIAM-ETH Peter Henrici Prize in 2019. He is a member of the Chinese Academy of Sciences, a fellow of SIAM, AMS and IOP. Weinan E has been an invited speaker at ICM, ICIAM as well as the AMS National Meeting. He has also been an invited speaker at APS, ACS, AIChe annual meetings and the American Conference of Theoretical Chemistry.

Weinan E’s main research interest is numerical algorithms, machine learning and multi-scale modeling, with applications to chemistry, material sciences and fluid mechanics. In recent years, he has also developed an interest in control theory.

Opinion dynamics models: A mathematical abstraction of individuals’ behaviours and interactions
(plenary talk “MoP_Audimax.1”, Monday 9:00, Audimax)

Abstract. The talk will focus on the main mechanisms influencing opinion dynamics, like homophily, mutual appraisal, and bounded confidence. Some classic opinion dynamics models, as well as some recent ones, will be presented. Interesting open problems as well as promising research directions will be proposed.

Short biography. M. Elena Valcher (Ph.D., 1995) is a Professor of Control Theory at the University of Padova since 2005.

She is an IEEE Fellow since 2012, a member of IFAC Technical Board since 2017 and the Italian Representative in EUCA since 2017. She is currently the co-General Chair (together with Andrea Serrani) of the 2022 IEEE Conference on Decision and Control.
She was the 2015 President of the IEEE Control Systems Society. She is the (founding) Editor-in-Chief of the IEEE Control Systems Letters (2017-), and she served as AE in several Editorial Boards, including IEEE Trans. Automatic Control, Systems Control Letters, Automatica, SIAM J. Control Optimization and IEEE Access (2014-2019). She is the author of more than 90  journal papers, and approximately 110  conference papers.

Her current research interest include multi-agent systems, social networks, positive systems, and Boolean control networks.

Lax-Phillips semigroups for nonlinear systems
(plenary talk “FrP_Audimax.1”, Friday 9:00, Audimax)

Abstract. We briefly recall the basics about Lax-Phillips semigroups for well-posed linear systems, and the definition of well-posed nonlinear systems via nonlinear Lax Phillips semigroups. Then we concentrate on two results concerning well-posed nonlinear systems:
We investigate a special class of nonlinear systems that are obtained by modifying the second order differential equation that is part of the description of conservative linear systems "out of thin air" introduced by M. Tucsnak and G. Weiss in 2003. The differential equation contains a nonlinear damping term that is maximal monotone and possibly set-valued. We show that this new class of nonlinear systems is incrementally scattering passive (hence well-posed). Our approach uses the theory of maximal monotone operators and the Crandall-Pazy theorem about nonlinear contraction semigroups, which we apply to the Lax-Phillips semigroup of the system.
We investigate the class of incrementally scattering passive nonlinear systems, as defined in some earlier papers of ours. We show that these can be defined by a differential inclusion and a formula defining the current output in term of the current state and the current input. Our approach uses the theory of maximal monotone operators.
The talk is based on joint work with Shantanu Singh.

Short biography. George Weiss is professor of control engineering in the School of EE, Tel Aviv University. He received the M.Eng. degree in control engineering from the Polytechnic Institute of Bucharest, Romania, in 1981, and the Ph.D. degree in applied mathematics from Weizmann Institute, Rehovot, Israel, in 1989. He has been with Brown University, Providence, RI, USA, Virginia Polytechnic Institute and State University, Blacksburg, VA, USA, Ben-Gurion University, Beer Sheva, Israel, University of Exeter, Exeter, UK, and Imperial College London, UK.

He is a co-author, with M. Tucsnak, of the book "Observation and Control for Operator Semigroups", Birkhäuser, 2009, and leading research projects for the European Commission and for the Israeli Ministry of Energy.

His research interests include distributed parameter systems, operator semigroups, passive and conservative systems (linear and nonlinear), power electronics, the stability of power grids, repetitive control, sampled data systems, and wind-driven power generators.

The role of the metric in numerical linear algebra and optimization
(semi-plenary talk “MoSP_H17.1”, Monday 14:00, H17)

Abstract. Many algorithms in everyday use implicitly employ the Euclidean inner product of the underlying space. While this is convenient and user-friendly on the one hand, it also turns out that the Euclidean metric may not be the one yielding the best performance of the respective algorithm. In this talk we revisit the role of the metric in a number of well-known algorithms in numerical linear algebra and optimization, and demonstrate the potential of user-defined metrics in each case.

Short biography. Roland Herzog has been Professor for Scientific Computing and Optimization at the Interdisciplinary Center for Scientific Computing of the University of Heidelberg, Germany, since 2021. Before that, he held positions at the Technische Universität Chemnitz, Germany, at RICAM in Linz, Austria, at the Karl Franzens University Graz, Austria, where he obtained his Habilitation in 2008, and at the University of Bayreuth, Germany, where he obtained his Ph.D. in 2003. He also held a visiting position at the University of British Columbia, Vancouver, Canada.

Prof. Herzog is Editorial board member for Advances in Discrete and Continuous Models, SMAI Journal of Computational Mathematics, SIAM Journal on Control and Optimization, SIAM Journal on Numerical Analysis, Journal of Optimization Theory and Applications, Electronic Transactions on Numerical Analysis, OPTPDE Problem Collection, and Optimization Methods and Software.

His research interests lie in the area of optimal control and optimization with partial differential equations.

The densities of good codes in various metric spaces
(semi-plenary talk “ThSP_H18.1”, Thursday 14:00, H18)

Abstract. The densities of codes with certain properties have always been of interest in classical coding theory, in particular to understand how many of such codes exist and how likely a random code will have the prescribed properties. Further applications of density results of codes appear in code-based cryptography, where it is important that the set of codes with a certain property is large enough to outgo brute force attacks. In this talk we will present various density results for optimal or close-to-optimal codes in different metric spaces with different types of linearity. In particular, we will show when optimal codes in the Hamming, rank and sum-rank metric are dense and when they are sparse.

Short biography. Anna-Lena Horlemann-Trautmann received the Diploma degree in mathematics from the University of Bochum, Germany, in 2007 and the Ph.D. degree in mathematics from the University of Zurich, Switzerland, in 2013. From 2013 until 2015 she was a research fellow at the Department of Electrical and Electronic Engineering at the University of Melbourne and at the Department of Electrical and Computer Systems Engineering at Monash University, both in Melbourne, Australia. She was a Postdoctoral fellow at the EPF Lausanne, Switzerland, from 2015 until 2017, before becoming an assistant professor for mathematics and information technology at the University of St. Gallen in Switzerland. Since 2021 she is an associate professor at the School of Computer Science at the University of St. Gallen.

Professor Horlemann-Trautmann is a member of the Editorial Board of Advances in Mathematics of Communication and an Associate Editor for the IEEE Transactions on Information Theory.

Her research interests lie in Algebraic Coding Theory and post-quantum cryptography.

Global Optimal Control: Opportunities and Challenges
(semi-plenary talk “FrSP_H18.1”, Friday 14:00, H18)

Abstract. Optimal control theory, algorithms, and software for analyzing and computing local solutions of linear and nonlinear optimal control problems have reached a high level of maturity, finding their way into industry. In the context of many applications, locally optimal control inputs can be computed within the milli- and microsecond range. This is in sharp contrast to the development of algorithms for locating global minimizers of non-convex optimal control problems, which is hindered by several key issues, including the overall complexity of generic optimal control problems and their curse of dimensionality. This talk reviews and discusses recent solutions that address these rather fundamental challenges including novel types of Branch & Lift methods as well as modern Koopman-Pontryagin operator based lifting methods for global optimal control. Various numerical experiments will be used to illustrate the effectiveness of these approaches. The talk concludes with an assessment of the state of the art and highlights important avenues for future research.

Short biography. Boris Houska is an associate professor at the School of Information Science and Technology at ShanghaiTech University. He received a diploma in mathematics and physics from the University of Heidelberg in 2007, and a Ph.D. in Electrical Engineering from KU Leuven in 2011. From 2012 to 2013 he was a postdoctoral researcher at the Centre for Process Systems Engineering at Imperial College London. Subsequently, from 2013-14, he has worked as a research faculty member at Shanghai Jiao Tong University. Moreover, he has held visiting professor positions at the Freiburg Institute for Advanced Studies as well as at the Institute for Microsystems Engineering at the University of Freiburg (both in 2014) and various shorter academic visiting appointments, e.g., at UC Berkeley during Winter 2017 and Imperial College London during Summer 2018.

Boris Houska has been recipient of awards including ICCOPT Best Paper Prize for a Young Researcher in Continuous Optimization (Finalist, Top 3), a Marie-Curie Fellowship for the project Next Generation Algorithms for Robust and Global Optimization of Dynamic Systems, as well as a ShanghaiTech Excellent Professor Award from ShanghaiTech University. His paper on "ACADO Toolkit — An open source framework for automatic control and dynamic optimization" has been listed as highly cited paper by Web of Science.

His research interests include numerical optimization and optimal control, set-based robust and global optimization, as well as fast model predictive control algorithms.

Funnel control – history and perspectives
(semi-plenary talk “TuSP_H18.1”, Tuesday 14:00, H18)

Abstract. The control objective in funnel control is output feedback control such that the norm of the error e(t) of the closed-loop system remains inside a prespecified funnel with boundary ψ(t), i.e. ‖e(t)‖ < ψ(t) for all t > 0. In other words, prescribed transient behaviour as well as asymptotic accuracy is achieved. Typical features of funnel control are:

After two decades of high-gain adaptive control, funnel control was introduced in 2002. First results were on linear, single-input, single-output, time-invariant systems with relative degree one and being minimum phase. From then on feasibility of funnel control was shown for other system classes such as multi-input, multi-output, nonlinear, infinite dimensional, perturbed systems, unknown control directions – provided they have stable zero dynamics and satisfy certain assumptions on the high-frequency gain. A particular challenge was to show feasibility for systems with higher relative degree, and to design a funnel controllers for systems described by partial differential equations. Funnel control was applied to various applications such as control in chemical reactor models, industrial servo-systems, wind turbine systems, electrical circuits, to name but a few. Recently, funnel control has been investigated in combination with model predictive control and applied to magnetic levitation systems.

Short biography. Achim Ilchmann obtained the Ph.D. in Mathematics in 1987 at the Universität Bremen, supervised by Professor Diederich Hinrichsen, and received his Habilitation in 1993 from the Universität Hamburg. From 1996 until 2000 he was Lecturer and Reader in Applied Mathematics at the Dept. of Mathematics of the University of Exeter, UK. Since 2000 he holds a Chair in Mathematics at the Institut für Mathematik of the Technische Universität Ilmenau, Germany.

Professor Ilchmann supervised 10 Ph.D. students and published 100 refereed journal papers, 3 books on mathematics, and 2 books on baroque architecture. Since 2003 he is the principle organizer of the annual Elgersburg Workshop on Mathematical Systems Theory.

His research interests include time-varying linear systems, adaptive control of nonlinear systems, and differential-algebraic equations.

High-dimensional approximation of Hamilton-Jacobi-Bellman PDEs in deterministic optimal control: architectures, algorithms, and applications
(semi-plenary talk “FrSP_H19.1”, Friday 14:00, H19)

Abstract. Optimal feedback synthesis for nonlinear dynamics -a fundamental problem in optimal control- is enabled by solving fully nonlinear Hamilton-Jacobi-Bellman type PDEs arising in dynamic programming. While our theoretical understanding of dynamic programming and HJB PDEs has seen a remarkable development over the last decades, the numerical approximation of HJB-based feedback laws has remained largely an open problem due to the curse of dimensionality. More precisely, the associated HJB PDE must be solved over the state space of the dynamics, which is extremely high-dimensional in applications such as distributed parameter systems or agent-based models.
In this talk we will review recent approaches regarding the effective numerical approximation of very high-dimensional HJB PDEs. We will explore modern scientific computing methods based on tensor decompositions of the value function of the control problem, and the construction of data-driven schemes in supervised and semi-supervised learning environments. We will highlight some novel research directions at the intersection of control theory, scientific computing, and statistical machine learning.

Short biography. Dante Kalise is Senior Lecturer in Computational Optimisation and Control at the Department of Mathematics, Imperial College London, since 2021. He received B.Sc. and M.Sc. degrees (2008) from the Federico Santa María Technical University in Valparaíso, Chile, and a Ph.D. (2012) from the University of Bergen, Norway. Before joining Imperial, he was Assistant Professor in Applied Mathematics at the University of Nottingham, and held research positions at RICAM Linz, and at La Sapienza University of Rome.

He serves as Associate Editor of Mathematics of Control, Signals, and Systems, and of Advances in Continuous and Discrete Models.

Dr. Kalise's research interests lie at the interface between scientific computing, optimal control, and PDEs. His current research is centered around the analysis and design of computational methods for the solution of high-dimensional Hamilton-Jacobi-Bellman PDEs and applications in nonlinear feedback control for PDE dynamics and agent-based models across scales.

Discontinuous Feedbacks for Stabilization and Combined Stabilization and Safety
(semi-plenary talk “MoSP_H18.1”, Monday 14:00, H18)

Abstract. It has long been known that asymptotic controllability of a nonlinear system to a desired equilibrium or target set require discontinuous controllers for feedback stabilization, which, in turn, is equivalent to the existence of a nonsmooth control Lyapunov function. More recently, results combining stabilization and safety, captured by so-called barrier functions, have been proposed. This also gives rise to the need for discontinuous feedback controllers, though for slightly different reasons. In this talk, we summarise these results and present a hybrid feedback solution to the combined stabilization and safety problem for a non-trivial class of systems.

Short biography. Christopher M. Kellett received the B.Sc. degree in electrical engineering and mathematics from the University of California, Riverside, and the M.Sc. and Ph.D. degrees in electrical and computer engineering from the University of California, Santa Barbara in 1997, 2000, and 2002, respectively. Chris has held research positions with the Centre Automatique et Systèmes at École des Mines de Paris, Paris, France; the University of Melbourne; and the Hamilton Institute at National University of Ireland, Maynooth. From 2006 until 2020 he was with the University of Newcastle, Australia. Since February 2020 he has been the Director of the School of Engineering at the Australian National University.

Chris is a Senior Editor for the IEEE Transactions on Automatic Control and an Associate Editor for Mathematics of Control, Signals and Systems. He has previously served on the editorial boards for IEEE CSS Letters and the European Journal on Control. He has been the recipient of an Australian Research Council Future Fellowship (2011-2015), an Alexander von Humboldt Research Fellowship (2012-2013), the 2012 IET Control Theory and its Applications Premium Award, and the inaugural IFAC Foundation Award (2017).

His research interests are broadly in the area of systems and control theory and applications, with specific emphases on stability and robustness properties for nonlinear systems.

Control design for distributed parameter systems – the port Hamiltonian approach
(semi-plenary talk “MoSP_H19.1”, Monday 14:00, H19)

Abstract. This talk is concerned with the control of distributed parameter systems defined on a 1D spatial domain using the port Hamiltonian framework. We consider two different cases: when actuators and sensors are located within the spatial domain and when the actuator is situated at the boundary of the spatial domain, leading to a boundary control system (BCS). In the first case we show how dynamic extensions and structural invariants can be used to change the internal properties of the system when the system is fully actuated, and how it can be done in an approximate way when the system is actuated using piecewise continuous actuators stemming from the use of patches. Asymptotic stability is achieved using damping injection. In the boundary-controlled case we show how the closed loop energy function can be partially shaped, modifying the minimum and a part of the shape of this function and how damping injection can be used to guarantee asymptotic convergence. We end with some some extensions of the proposed results to irreversible thermodynamic systems.

Short biography. Yann Le Gorrec is full Professor at National Engineering Institute in Mechanics and Microtechnologies of Besançon, France. He is the director of the AS2M department of the FEMTO-ST institute. His current field of research is the control of nonlinear systems and Distributed Parameter Systems with an application to smart material based actuators, micro systems, irreversible thermodynamic systems and fluid structure interactions by using the port Hamiltonian framework.

He has co-authored more than 220 publications (among which more than 60 journal papers and 30 invited plenary talks) and has been the coordinator of numerous collaborative research projects. He is an Associate Editor for IEEE Transactions on Automatic Control and Systems and Control Letters.

He has been the chair of the IEEE Technical Committee DPS from 2016 to 2019 and is currently the chair of the IFAC TC 2.6 on Distributed Parameter Systems and member of IFAC TC2.1 Control Design, TC2.3 Control of Nonlinear Systems , and TC2.6 Distributed Parameter Systems.

Scalable distributed control and learning of networked dynamical systems
(semi-plenary talk “ThSP_H19.1”, Thursday 14:00, H19)

Abstract. Recent radical evolution in distributed sensing, computation, communication, and actuation has fostered the emergence of cyber-physical network systems. Regardless of the specific application, one central goal is to shape the network collective behavior through the design of admissible local decision-making algorithms. This is nontrivial due to various challenges such as the local connectivity, system complexity and uncertainty, limited information structure, and the complex intertwined physics and human interactions.
In this talk, I will present our recent progress in formally advancing the systematic design of distributed coordination in network systems via harnessing special properties of the underlying problems and systems. In particular, we will present three examples and discuss three type of properties, i) how to use network structure to ensure the performance of the local controllers; ii) how to use the information and communication structure to develop distributed learning rules; iii) how to use domain-specific properties to further improve the efficiency of the distributed control and learning algorithms.

Short biography. Na Li is a Gordon McKay professor in Electrical Engineering and Applied Mathematics at Harvard University. She received her B.S. degree in Mathematics from Zhejiang University in 2007 and Ph.D. degree in Control and Dynamical systems from California Institute of Technology in 2013. She was a postdoctoral associate at the Massachusetts Institute of Technology 2013-2014. Her research lies in the control, learning, and optimization of networked systems, including theory development, algorithm design, and applications to cyber-physical societal systems.

She received NSF career award (2016), AFSOR Young Investigator Award (2017), ONR Young Investigator Award (2019), Donald P. Eckman Award (2019), McDonald Mentoring Award (2020), along with some other awards.

Compressed sensing and maximum hands-off control
(semi-plenary talk “FrSP_H17.1”, Friday 14:00, H17)

Abstract. Compressed sensing has been actively researched in the field of signal processing and machine learning. More recently, the method has been applied to control problems. In this talk, we will briefly review compressed sensing for vectors, and then introduce the maximum hands-off control for continuous-time systems, which aims at finding the sparsest control under control constraints.

Short biography. Masaaki Nagahara received the bachelor's degree in engineering from Kobe University in 1998, and the master's degree and the doctoral degree in informatics from Kyoto University in 2000 and 2003, respectively. He is currently a Full Professor with the Institute of Environmental Science and Technology, The University of Kitakyushu. He has been a visiting professor with Indian Institute of Technology (IIT) Bombay since 2017, and IIT Guwahati since 2020.

He received the Transition to Practice Award in 2012 and the George S. Axelby Outstanding Paper Award in 2018 from IEEE Control Systems Society. He also received the Young Authors Award in 1999, Best Paper Award in 2012, Best Book Authors Award in 2016, and SICE Control Division Research Award (Kimura Award) in 2020 from SICE, and the Best Tutorial Paper Award in 2014 from IEICE Communications Society. He is a senior member of IEEE, and a member of SICE, ISCIE, IEICE, JSME, and RSJ.

His research interests include control theory, machine learning, and signal processing.

Extended (differential) balancing for model reduction of linear and nonlinear dynamical systems
(semi-plenary talk “ThSP_H17.1”, Thursday 14:00, H17)

Abstract. In this talk, we will develop extended balancing and its structure preservation possibilities for linear systems, as well as extended balancing theory for nonlinear systems in the contraction framework. For the latter, we introduce the concept of the extended differential observability Gramian and inverse of the extended differential controllability Gramian for nonlinear dynamical systems and show their correspondence with generalized differential Gramians. We also provide how extended (differential) balancing can be utilized for model reduction to get a smaller apriori error bound in comparison with generalized (differential balancing). We will focus on preserving the structure of a port-Hamiltonian system with help of extended balancing in both the linear and nonlinear systems setting.

Short biography. Jacquelien Scherpen received her MSc and PhD M.Sc. and Ph.D. degrees in 1990 and 1994 from the University of Twente, the Netherlands. She then joined Delft University of Technology and moved in 2006 to the University of Groningen as a professor in Systems and Control Engineering at the Engineering and Technology institute Groningen (ENTEG), Faculty of Science and Engineering at the University of Groningen, the Netherlands. From 2013 til 2019 she was scientific director of ENTEG. She is currently director of the Groningen Engineering Center, and Captain of Science of the Dutch top sector High Tech Systems and Materials (HTSM). Jacquelien has held various visiting research positions, such as at the University of Tokyo, and Kyoto University, Japan, Université de Compiegne, and SUPÉLEC, Gif-sur-Yvette, France, and Old Dominion University, VA, USA.

Jacquelien has been and is at the editorial board of a few international journals among which the IEEE Transactions on Automatic Control, and the International Journal of Robust and Nonlinear Control. She received the 2017-2020 Automatica Best Paper Prize. In 2019 she received a royal distinction and is appointed Knight in the Order of the Netherlands Lion, and she is a fellow of IEEE. She has been active at the International Federation of Automatic Control (IFAC), and is currently member of the IFAC council. She is a member of the Board of Governors of the IEEE Control Systems Society, and was chair of the IEEE CSS standing committee on Women in Control in 2020. From 2020 to 2021 she is president of the European Control Association (EUCA).

Her current research interests include model reduction methods for networks, nonlinear model reduction methods, nonlinear control methods, modeling and control of physical systems with applications to electrical circuits, electromechanical systems, mechanical systems, smart energy networks and distributed optimal control applications to smart grids.

A General Framework for Machine Learning-based Optimization Under Uncertainty
(semi-plenary talk “TuSP_H19.1”, Tuesday 14:00, H19)

Abstract. Approaches to decision making and learning mainly rely on optimization techniques to achieve “best” values for parameters and decision variables. In most practical settings, however, the optimization takes place in the presence of uncertainty about model correctness, data relevance, and numerous other factors that influence the resulting solutions. For complex processes modeled by nonlinear ordinary and partial differential equations, the incorporation of these uncertainties typically results in high or even infinite dimensional problems in terms of the uncertain parameters as well as the optimization variables, which in many cases are not solvable with current state of the art methods. One promising potential remedy to this issue lies in the approximation of the forward problems using novel techniques arising in uncertainty quantification and machine learning.
We propose in this talk a general framework for machine learning based optimization under uncertainty and inverse problems. Our approach replaces the complex forward model by a surrogate, e.g. a neural network, which is learned simultaneously in a one-shot sense when estimating the unknown parameters from data or solving the optimal control problem. By establishing a link to the Bayesian approach, an algorithmic framework is developed which ensures the feasibility of the parameter estimate / control w.r. to the forward model.
This is joint work with Philipp Guth (U Mannheim) and Simon Weissmann (U Heidelberg).

Short biography. Claudia Schillings is Professor in Numerical Analysis of stochastic and deterministic PDEs at the Institute for Mathematics, FU Berlin. Claudia Schillings received her Ph.D. degree from the Department of Mathematics, University of Trier (Germany) in 2011. After two and half years of postdoctoral activity at ETH Zurich (Switzerland) and two years at the University of Warwick (UK), she was visiting professor at the Humboldt University Berlin (Germany) in 2015-2016, professor at the University of Mannheim in 2017-2022 and then moved to Berlin.

Convex invertible cones and Nevanlinna-Pick interpolation
(semi-plenary talk “TuSP_H17.1”, Tuesday 14:00, H17)

Abstract. Nevanlinna-Pick interpolation developed from a topic in classical complex analysis to a useful tool for solving various problems in control theory and electrical engineering. Over the years many extensions of the original problem were considered, including extensions to different function spaces, nonstationary problems, several variable settings and interpolation with matrix and operator points. In this talk we discuss a variation on Nevanlinna-Pick interpolation for positive real odd functions evaluated in real matrix points. This problem was studied by Cohen and Lewkowicz using convex invertible cones and the Lyapunov order, making interesting connections with stability theory. The solution requires an analysis of linear matrix maps using representations that go back to work of R.D. Hill from the 1970s and focusses, in particular, on the question when positive linear matrix maps are completely positive. If time permits, some possible extensions to multidimensional systems will briefly be discussed.

Short biography. Sanne ter Horst received his Ph.D. in Mathematics (operator theory) at the VU Amsterdam in the Netherlands in 2007. During his Ph.D. studies he received a training in system and control theory from the Dutch Institute of Systems and Control. After a postdoctoral fellowship at Virginia Tech and assistant professor positions at Utrecht University and Radboud University Nijmegen, he moved to South Africa where he is now a full professor in mathematics at North-West University.

He is currently an associate editor at Quaestiones Mathematicae and Complex Analysis and Operator Theory, where he manages the Linear Operators and Linear Systems section, and has been on the council of the South African Mathematical Society since 2013 in various portfolios.

The focus of his research is on operator theory and matrix analysis methods with applications in system and control theory. More specifically, he has recently worked on metric constrained interpolation, infinite dimensional systems, structured systems and control problems, noncommutative multidimensional systems, and inverse problems.