Abstract
This talk describes a hierarchy of controllers needed to control the future weather-driven and decentralized energy system. Traditionally, power systems are operated and planned such that the production follows the demand. However, an efficient implementation of the weather-driven energy system calls for a system where demand follows production. This calls for control-based methods for activating flexibility at essentially all types of end-users and at all aggregation levels. It will be argued that in order to maximize the flexibility options, all aspects of energy systems integration and sector coupling, like the energy-water nexus, have to be considered. Today electricity markets are based on the principles of merit-order bidding and clearing. It will be explained why such conventional market principles are not suitable for activating end-user flexibility. Instead it is suggested to use a hierarchy of controllers. A key element is the so-called Flexibility Function which contains a description of the dynamics and variations that can be handled by modern control theory. The entire framework, called the ‘Smart-Energy OS’, represents a digitalisation of the energy systems, and it will be demonstrated that this framework can be used for essentially all balancing and ancillary service for power systems with a large penetration of wind and solar power.

Biography
Henrik Madsen is the Section Head and Professor in Stochastic Dynamical Systems at the Technical University of Denmark in Lyngby (near Copenhagen), Section for Dynamical Systems at the Department for Applied Mathematics and Computer Sciences.
Since January 2014 I’m heading a National Strategic Research Centre (DSF Centre) entitled: Centre for IT-Intelligent Energy Systems in Cities (CITIES). This centre aims at being a leading research centre related to Smart Cities and Green IT activities.
Henrik Madsen have been guest lecturing at a number of universities, such as University of Lund (Mathematics), Fourier University in Grenoble (Section for Mathematical Statistics), University of Copenhagen (Mathematical Statistics), Technical University of Münich (TUM), Iowa State University (Department of Statistics), University of Almeria in Spain (Department of Physics), Charles III University of Madrid in Leganés (Department of Statistics), and many other places.

Abstract
Cyber-Physical Systems (CPS) that consist of digital (cyber) and physical components are prevalent in all sectors of human endeavor, including robotics, power generation and distribution, transport, aerospace, medical instrumentation, process industry, mining, and so on. A class of CPS that has attracted a lot of attention over the past 25 years are the so-called Networked Control Systems (NCS) where the actuator and sensor data is transmitted over packet-based digital communication networks. This technology allows us to design systems that are cheaper, easier to assemble and maintain and that are lower in volume and weight. However, the analysis and design for NCS is much harder since the communication network typically acts as a communication bottleneck and introduces undesirable effects, like non-equidistant sampling, data dropouts, communication and computation delays and quantization. Indeed, modern NCS technologies combine control, communication and computation in non-classical manners that require new analysis and design methodologies to ensure their stability, robustness and performance. This talk will overview some of my team’s work over the past 20 years that concentrates on modelling, control, estimation and robustness of such systems. In particular, Hybrid Dynamical Systems provide a convenient modeling, analysis and design framework that allows us to cover a broad range of network communication protocols, transmission mechanisms (time or event triggering) and network uncertainty (data dropouts and delays). The focus of this talk is an emulation approach to controller/estimator design for general nonlinear plants that relies on Lyapunov stability theory for hybrid dynamical systems.

Biography
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.

Abstract
Recent advances in computers and numerical solution methods have expanded the application of model predictive control (MPC), which solves optimal control problems in real time to perform feedback control. Particularly, MPC has been gaining attention for its applications to complex nonlinear mechanical systems. Additionally, software tools for MPC have rapidly developed to facilitate its implementation. In this talk, I will present a comprehensive overview of real-time optimization algorithms, software tools, and applications of MPC, including recent advances exploiting parallel computation and an application to whole-body control of a quadruped.

Biography
Toshiyuki Ohtsuka is a Professor at the Graduate School of Informatics, Kyoto University, Japan. He received the B.Eng., M.Eng, and Ph.D. degrees from the Tokyo Metropolitan Institute of Technology, Japan, in 1990, 1992, and 1995, respectively. From 1995 to 1999, he worked as an Assistant Professor at the University of Tsukuba. In 1999, he joined Osaka University as an Assistant Professor at the Graduate School of Engineering. Then, in 2007, he moved to the Graduate School of Engineering Science at the same university as a Professor. In 2013, he joined Kyoto University as a Professor at the Graduate School of Informatics. His research interests include nonlinear control theory and real-time optimization methods with applications to mechanical systems such as drones, robots, and automobiles. He received the SICE Outstanding Paper Award in 2004 and 2013, the SICE Outstanding Book Award in 2012, and SICE Control Division Pioneer Award and Kimura Award in 2006 and 2014, respectively. He is a member of SICE and a Senior Member of IEEE and AIAA. He is the NOC Chair of the 8th IFAC Conference on Nonlinear Model Predictive Control (NMPC 2024).

Abstract
Aerial robotics have become ubiquitous, but (like most robots) they still struggle to operate at high speed in unstructured, cramped environments. By considering a vehicle’s mechanical design simultaneously with the design of controls and automation algorithms, we have more degrees of freedoms to find creative solutions to problems. In this talk I will present some of my group’s work on enhancing aerial robots, including purely algorithmic approaches (“how can I do more with the hardware I already have?”) and with hardware co-design (“how can I change the vehicle so that the hard problem is actually easy?”). Two challenges for aerial robots will motivate us: first: flight through narrow, unstructured environments, and second: long duration and range flight within the constraints of battery-electric power. For flight through narrow environments, I will present an algorithmic approach for high speed path planning that incorporates perception uncertainty, and can be used on a standard drone. We will then present two alternative approaches that modify the system design: one a vehicle that can change its shape to fit through narrower spaces, and a second that is highly collision resilient, and for whom collisions are therefore neither mission- nor safety-critical.
For overcoming energetic challenges, we will present a strategy for real-time optimization of flight characteristics for a vehicle, specifically using extremum seeking control to modify the system airspeed and yaw angle; an algorithm that can be applied to any aerial robot. We then again show two design modifications to work around the problem — first, a morphing system that can reduce its drag area at speed, and secondly a system capable of mid-air battery replacement for indefinite flight.

Biography
Mark W. Mueller is an assistant professor of Mechanical Engineering at the University of California, Berkeley. His research focuses on the dynamics, design, and control of aerial robots. He joined UC Berkeley in September 2016. Prior to that, he completed his graduate studies at ETH Zurich in Switzerland, and received his undergraduate degree from the University of Pretoria in South Africa.

Abstract
The future of manufacturing equipment and scientific instruments hinges on the ability to perform precise and fast motions. Examples of such mechatronic systems include wafer scanners, printing systems, pick-and-place machines, microscopes, and telescopes. These systems are subject to ever-increasing speed, accuracy, and flexibility requirements. Learning from data provides major opportunities to meet these requirements. Identification, learning, and control methodologies are developed that can deal with the large complexity in envisaged future mechatronic systems. These are successfully implemented on state-of-the-art mechatronic systems. The new results pave the way for new, revolutionary, data-intensive mechatronic designs with a massive number of actuators and sensors.

Biography
Tom Oomen is full professor with the Department of Mechanical Engineering at the Eindhoven University of Technology. He is also a part-time full professor with the Delft University of Technology. He received the M.Sc. degree (cum laude) and Ph.D. degree from the Eindhoven University of Technology, Eindhoven, The Netherlands. He held visiting positions at KTH, Stockholm, Sweden, and at The University of Newcastle, Australia. He is a recipient of the 7th Grand Nagamori Award, the Corus Young Talent Graduation Award, the IFAC 2019 TC 4.2 Mechatronics Young Research Award, the 2015 IEEE Transactions on Control Systems Technology Outstanding Paper Award, the 2017 IFAC Mechatronics Best Paper Award, the 2019 IEEJ Journal of Industry Applications Best Paper Award, and recipient of a Veni and Vidi personal grant. He is currently a Senior Editor of IEEE Control Systems Letters (L-CSS) and Associate Editor of IFAC Mechatronics, and he has served on the editorial boards of the IEEE Control Systems Letters (L-CSS) and IEEE Transactions on Control Systems Technology. He has also been vice-chair for IFAC TC 4.2 and a member of the Eindhoven Young Academy of Engineering. His research interests are in the field of data-driven modeling, learning, and control, with applications in precision mechatronics.

Abstract
Range anxiety and gasoline thinking create a strong demand for ultra-fast DC charging of electric vehicles. However, there are a range of limiting factors, from battery cells and vehicles to charging infrastructures. This talk introduces enablers for ultra-fast DC charging of electric vehicles that combat such limiting factors.

Biography
Naehyuck Chang was a Professor at the School of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST) from 2021. Before he joined KAIST, he was with the Department of Computer Science and Engineering, Seoul National University from 1997 to 2014. Dr. Chang also served as a Vice Dean of College of Engineering, Seoul National University from 2011 to 2013. His current research interests include low-power embedded systems and Design Automation of Things such as systematic design and optimization of energy storage systems and electric vehicles.

Dr. Chang is an ACM Fellow and an IEEE Fellow for contribution to low-power design. Dr. Chang is the Editor-in-Chief of the ACM (Association for Computing Machinery) Transactions on Design Automation of Electronics Systems, and an Associate Editor of IEEE Transactions on Very Large Scale Integration Systems. He also served for IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, IEEE Embedded Systems Letters, ACM Transactions on Embedded Computing Systems, and so on, as an Associate Editor. Dr. Chang is (was) the General Co-Chair of VLSI-SoC (Very Large Scale Integration), ICCD (International Conference on Computer Design) 2014 and 2015, ISLPED 2011, etc. Dr. Chang is (was) Technical Program (Co-) Chair of DAC (Design Automation Conference) 2016, ASP-DAC (Asia and South Pacific Design Automation Conference) 2015, ICCD 2014, CODES+ISSS (Hardware Software Codesign and System Synthesis) 2012, ISLPED 2009, etc. Dr. Chang is the Past Chair of ACM SIGDA (Special Interest Group on Design Automation).