MINI COURSE: Mouhacine Benosman

Senior Principal Research Scientist, Mitsubishi Electric Research Laboratories (MERL)

A hybrid approach to control: classical control theory meets machine learning theory

Until recently, one could classify control approaches into two main paradigms. The first is the classical machine learning (ML) control paradigm that heavily relies on data, e.g., classical approximate dynamic programming (ADP), reinforcement learning (RL) methods, deep neural networks (DNN), and deep RL. The main advantages of these methods are efficiency and flexibility due to the increased availability of data and computation power. On the other hand, these methods lack performance guarantees, such us stability, boundedness of signals, a.k.a., safety, and general robustness.

The second paradigm is the classical control theory approach, which relies on dynamical systems theory, e.g., robust control theory, adaptive control theory, Lyapunov-based design, etc. In this case the pros and cons are somewhat reversed. Some examples of advantages of this paradigm include the rigor of mathematical analysis and the performance guarantees in term of stability, boundedness of signals, and robustness. However, one of the main disadvantages of this approach is the lack of flexibility or generalizability since the model of the system must satisfy very specific properties.

In the past 10 or so years, several efforts have attempted to merge these two paradigms.  The result is what we refer to as Learning-based control methods. These methods use tools from classical control theory together with tools from ML theory. The aim is to design ‘hybrid’ learning-based controllers that take advantage of the flexibility of ML data-driven methods, while maintaining the stability, safety, and robustness guarantees from control theory.

This short course will concentrate on learning-based control methods. We first present recent results in the field of learning-based adaptive control, where classical model-based adaptive control methods are merged with data-driven estimation methods, e.g., extremum seeking control (ESC), Gaussian processes (GP) optimization, and reinforcement learning (RL). We then discuss some recent results on robust constrained model-based RL that use tools from nonlinear control theory to guarantee stability, robustness and safety. We will cover the main theoretical aspects of these approaches and finish the course with a few examples of industrial applications.

Short Biography :

Before coming to Mitsubishi Electric Research Laboratories (MERL) in 2010, Mouhacine worked at universities in Reims University, France and Strathclyde University, Scotland, and the National University of Singapore.

His research interests include modeling and control of flexible robotic manipulators, nonlinear robust and fault tolerant control, multi-agent control with applications to smart-grid and robotics, estimation and control of partial differential equations with applications to thermo-fluid models, learning-based adaptive control for nonlinear systems, and control-theory based optimization algorithms with application to machine learning.

Mouhacine has published more than 50 peer-reviewed journal articles and conference papers, and has more than 20 patents in the field of mechatronics systems control. He is a senior member of the IEEE, Associate Editor of the Journal of Optimization Theory and Applications, Associate Editor of the Journal of Advanced Control for Applications, Associate Editor of the IEEE Control Systems Letters, and Senior Editor of the International Journal of Adaptive Control and Signal Processing.

PLENARY LECTURE: David Howey

 

Professor of Engineering Science at the University of Oxford, UK

Data-driven battery health diagnosis in real-world applications

Accurate diagnostics and prognostics of battery health improves overall system performance. This allows industry to unlock value by detecting faults and improving maintenance and logistics, extending operational range, and understanding asset depreciation. However, battery aging is complex and caused by many interacting factors. Two key questions arise: first, how to handle modelling challenges, including parameter variability and nonlinearities, in methods for online estimation of state of health. Second, how to develop validated predictions of future health, where key issues include coping with variable usage scenarios, and cell-to-cell behavioural differences. This talk will discuss recent approaches to tackle some of these exciting topics, particularly focusing on diagnostics from field data, including the combining of non-parametric and parametric models to allow flexibility in model fitting from data, whilst retaining the benefits of equivalent circuit and physical models.

Short Biography:

David Howey is Professor of Engineering Science at the University of Oxford, UK. He leads a group researching on modelling and control of energy storage systems, with a particular focus on Li-ion batteries for electric vehicles and grid/off-grid storage. He received the MEng degree in Electrical and Information Sciences from the University of Cambridge in 2002 and his PhD from Imperial College London in 2010. Since 2010 he has co-authored 80+ peer-reviewed journal and conference articles, and 5 patents. He is an editorial board member of IEEE Transactions on Industrial Informatics and the new OUP journal Oxford Open Energy, and is co-founder of the Oxford Battery Modelling Symposium. He is the recipient of recent funding from EPSRC, InnovateUK, UKRI, Faraday Institution, Continental AG and Siemens, and he co-leads control and estimation tasks in the Faraday Institution Multiscale Modelling project. Howey is also academic lead for the £40m Energy Superhub Oxford that is building a transmission connected 50 MWh hybrid battery. He previously led the Faraday Institution “UK EV and Battery Production Potential” project (with McKinsey), and was academic lead in InnovateUK projects on battery re-use (EP/P510737/1) and solar home systems in Africa (EP/R035822/1), and a $1.2m Korean project on microgrids, plus Co-I in EPSRC projects TRENDS, FUTURE vehicles, STABLE-NET and RHYTHM. Professor Howey is co-founder of Brill Power Ltd., a company spun-out of his lab in 2016 focused on advanced battery management system topologies. They have raised significant early stage funding and adopted several patents from his group. Howey also won a Samsung GRO Award on modelling leading to two R&D contracts and a multi-year collaboration, with results patented by Samsung Electronics.

PLENARY LECTURE: Nicanor Quijano

Professor of control and automation systems at the Universidad de los Andes, Colombia

The Role of Population Games and Evolutionary Dynamics in Control

Recently, there has been in the control community an increasing interest in studying large-scale distributed systems (LSDS). Several techniques have been developed, wishing to address the main challenges found in LSDS. One way to approach this type of problems is to use game-theoretical methods. Game theory shares some common points with control systems problems, in particular of distributed topology, where the interconnection of different elements (agents) leads to a global behavior depending on the local interaction of these agents. Evolutionary game theory (EGT) is one type of dynamic games that has been used to design distributed controllers for different applications like control of water systems, charging of electric vehicles, and synchronization of isolated microgrids. The aim of this talk is to present and discuss relevant advances and analytical methodologies in population games and evolutionary dynamics, and its applications for solving control problems.

Short Biography:

Nicanor Quijano (IEEE Senior Member) received his B.S. degree in Electronics Engineering from Pontificia Universidad Javeriana (PUJ), Bogotá, Colombia, in 1999. He received the M.S. and PhD degrees in Electrical and Computer Engineering from The Ohio State University, in 2002 and 2006, respectively. In 2007, he joined the Electrical and Electronics Engineering Department, Universidad de los Andes (UAndes), Bogotá, Colombia as an Assistant Professor. He is currently a Full Professor, the director of the research group in control and automation systems (GIAP, UAndes), and an associate editor for the IEEE Transactions on Control Systems Technology, the Journal of Modern Power Systems and Clean Energy, and Energy Systems. He has been a member of the Board of Governors of the IEEE Control Systems Society (CSS) for the 2014 period, and he was the chair of the IEEE CSS, Colombia for the 2011-2013 period. He has published more than 100 scientific papers (journal papers, international conference papers, book chapters), he has co-advised the best European PhD thesis in the control systems area in 2017, and he is the co-author of the best paper of the ISA Transactions, 2018. In 2021, he obtained the Experienced Research Award from the School of Engineering, UAndes. Currently his research interests include hierarchical and distributed network optimization methods for control using learning, bio-inspired, and game-theoretical techniques for dynamic resource allocation problems, especially those in energy, water, agriculture, and transportation.

PLENARY: Ilya Kolmanovsky

 

Professor in the Department of Aerospace Engineering, The University of Michigan, USA

Perspectives, Challenges and Opportunities in Control of Systems with Constraints

Constraints represent bounds imposed on system state, output and control signals that must be satisfied during system operation.  Examples of constraints include safety limits, comfort limits and obstacle avoidance requirements. Constraints are important considerations in many engineering systems such as aircraft, spacecraft and automotive vehicles.  Achieving high performance in systems with constraints is challenging: The effective controllers must be nonlinear and often have to be based on prediction and optimization.  The interest in handling computational (cyber-) constraints in addition to physical constraints has also been increasing concomitantly with the growing research in cyber-physical systems.   

The speaker will present several perspectives on control of systems with constraints, drawn from his experience with the relevant theory and applications,  and discuss the underlying challenges and opportunities.  These include, in particular, the development of add-on schemes of various kind that could be augmented to the nominal system to protect it from constraint violation, the interplay between closed-loop stability, performance  and computations in model predictive control, integration of constrained control and learning,  and dealing with constraints in situations in which eventual constraint violation is unavoidable.  Applications to spacecraft, aircraft and automotive systems will be considered throughout for illustration.

Short Biography:

Professor Ilya V. Kolmanovsky has received his Ph.D. degree in Aerospace Engineering in 1995, his M.S. degree in Aerospace Engineering in 1993 and his M.A. degree in Mathematics in 1995, all from the University of Michigan, Ann Arbor. He is presently a full professor in the Department of Aerospace Engineering at the University of Michigan. Professor Kolmanovsky’s research interests are in control theory for systems with state and control constraints, and in control applications to aerospace and automotive systems.  Before joining the University of Michigan in January 2010, he was with Ford Research and Advanced Engineering in Dearborn, Michigan for close to 15 years. He is a Fellow of IEEE, an Associate Fellow of AIAA, a past recipient of the Donald P. Eckman Award of American Automatic Control Council, of 2002 and 2016 IEEE Transactions on Control Systems Technology Outstanding Paper Awards, of SICE Technology Award, of several technical achievement, innovation and publication awards of Ford Research and Advanced Engineering.  His publication record includes over 200 journal articles, over 400 conference papers, 20 book chapters, 3 edited books, as well as 104 United States patents.  He serves as a Senior Editor for IEEE Transactions on Control Systems Technology.

System Identification - a frequency domain approach

In memoriam Rik Pintelon

Prof. Rik Pintelon passed away last year in September 2021. He was an exceptional person, both from a scientific and from a human perspective. In this plenary talk, a selection of Rik’s achievements as a person and as a scientist will be given, and on-going research work along the lines he initiated will be presented.

Rik played a key role in elevating frequency domain based system identification to a mature data-driven modelling tool. His work was highly recognised in the scientific community. He has been the recipient of the Joseph F. Keithley award in 2012 from the IEEE Instrumentation and Measurement society, he received the degree of Doctor of Science (DSc) from the University of Warwick where he has also been honorary professor from 2013 to 2018, and received multiple awards for publications including the 2008 IOP outstanding paper award in Measurement Science & Technology, the 2014 Martin Black prize in Physiological Measurement and the 2014 Andy Chi award in IEEE transactions on Instrumentation and Measurement.

Rik’s work revolved around parametric and non-parametric identification of dynamic systems, with a very special attention to the estimation of uncertainties. Specifically, he played a leading and profound role in developing detection and quantification techniques of non-idealities – noise, nonlinear distortions and time-varying contributions – in Frequency Response Function measurement techniques. He successfully transferred this expertise to many colleagues and researchers in Belgium and across the globe, while remaining open minded and meticulous in his derivations.

Rik attached a warm attention to the human aspects of research and education. As the chairman of the Doctoral Committee at the Engineering Faculty of the VUB he vice-chaired between 30 and 40 PhD defenses per year, assessing the scientific integrity and the fair evaluations of the candidates. During that time, he instigated exemplary initiatives to improve and maintain the well-being of PhD researchers, amongst others by monitoring their educational and administrative load and equal chances, in all confidentiality.

Rik has been a role model to many of us, a great colleague and friend, always to be remembered.

Biography

John Lataire (S’06–M’11) was born in Brussels, Belgium, in 1983. He received the Electrical Engineer degree in electronics and information processing and the Ph.D. degree in engineering sciences (Doctor in de Ingenieurswetenschappen) from the Vrije Universiteit Brussel, Brussels, in 2006 and 2011, respectively.  From October 2007 to October 2011, he was on a Ph.D. fellowship from the Research Foundation—Flanders (FWO). Since August 2006, he has been working as a Researcher with the Department ELEC-VUB, Brussels. Dr. Lataire is the coauthor of more than 40 articles in refereed international journals. He received the 2008 IOP outstanding paper award (best paper in Measurement Science & Technology), the Best Junior Presentation Award 2010 at the 29th Benelux Meeting on Systems and Control, was the co-recipient of the 2014 Andy Chi award (best paper in IEEE Trans. on Instrumentation and Measurement), and was the recipient of the 2016 J. Barry Oakes Advancement Award (from the IEEE Instrumentation and Measurement society). His main interests include the frequency domain formulation of the identification of dynamic systems, with a specific focus on the identification of time-varying systems, and the use of kernel-based regression in system identification.

Noël Hallemans was born in Brussels in 1996. In 2019, he obtained his master's degree in Electrical Engineering with the highest distinction from both the Vrije Universiteit Brussel (VUB) and the Université Libre de Bruxelles (ULB). His master thesis dissertation, entitled 'Nonparametric Identification of Linear Time-Varying Systems using Gaussian Processes', won that year's Brussels Engineering Alumni prize for best master thesis. Since September 2019 he has been working as a researcher with the ELEC department (VUB) under supervision of professors Rik Pintelon and John Lataire. His research focuses on frequency domain data-driven modelling of dynamical systems, in particular on the best linear time-varying approximations and kernel-based FRF estimators. Recently, he obtained an Eutopia grant for a scientific research stay at the University of Warwick, to apply these data-driven methods to energy storage devices.

Dries Peumans (Member, IEEE) was born in Brussels, Belgium, in 1992. He received the M.Sc. degree and the Ph.D. degree in Electrical Engineering (Electronics and Information Technology) from the Vrije Universiteit Brussel (VUB), Belgium, in 2015 and 2020, respectively. Since 2021, he is a postdoctoral researcher at the VUB and at the Advanced RF group of imec. Broadband characterization and modelling of mm-wave and sub-THz circuits and transceivers currently involve his research activities.