Keynote Speakers
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Professor Frank Allgöwer, IFAC president
IFAC president, Director of the Institute for Systems Theory and Automatic Control and Professor, Mechanical Engineering Department, University of Stuttgart, Germany.
Talk Title: Current trends in model predictive control
During the past decades model predictive control (MPC) has become a preferred control strategy for the control of a large number of industrial control problems from distillation control to autonomous driving. Computational issues, application aspects and systems theoretic properties of MPC (like stability and robustness) are rather well understood by now and the theory is well developed even for nonlinear systems. However, during the past couple of years there have been some exciting new trends in MPC that promise to change the field in a lasting way. In this overview presentation we will give an introduction to and an overview over the general field of model predictive control focusing on the new trends.Abstract
Most prominently one of those trends concerns the control objective to be achieved. In standard MPC formulations, the considered control objective is typically the stabilization of some (given) setpoint or trajectory to be tracked. In contrast, the main focus in so-called "economic MPC" is on closed-loop performance where the cost to be optimized is directly related to some economic objective. This shift in the typical control task to be solved is especially of interest for many industrial applications like robot control, autonomous mobility, or industrial production processes in the framework of Industry 4.0, and will be discussed in the talk.
Secondly, interesting new results for "distributed economic model predictive control" for the control of networks of systems have been developed recently and will be presented in the talk.
And, thirdly, the new possibilities arising from data science and learning have also led to exciting new developments in MPC that will also be briefly addressed in this presentation.
Short Biography
Frank Allgöwer is director of the Institute for Systems Theory and Automatic Control and professor in Mechanical Engineering at the University of Stuttgart in Germany. Frank's main interests in research and teaching are in the area of systems and control with a current emphasis on the development of new methods for optimization-based control, networks of systems, data-based control and systems biology. Frank received several recognitions for his work including the IFAC Outstanding Service Award, the IEEE CSS Distinguished Member Award, the State Teaching Award of the German state of Baden-Württemberg, and the Leibniz Prize of the Deutsche Forschungsgemeinschaft. Frank is President of the International Federation of Automatic Control (IFAC) for the years 2017-2020. He was Editor for the journal Automatica from 2001 to 2015 and is editor for the Springer Lecture Notes in Control and Information Science book series and has published over 500 scientific articles. Since 2012 Frank serves a Vice-President of the German Research Foundation (DFG).
- Professor Manuel Giuliani, Professor of Embedded Cognitive AI for Robotics, Bristol Robotics, UK
Professor in Embedded Cognitive AI for Robotics at the Bristol Robotics Laboratory (BRL), University of the West of England, Bristol
Talk Title: Embodied Cognition for Human-Robot Interaction
Abstract
In the next decades robots will be used more and more in new application areas such us households, assisted living homes, and public spaces. Furthermore, more collaborative robots will be used professionally in the future, for example by workers on factory floors and by operators in extreme and hazardous environments. This means that there will be a growing user base of people who will interact with robots on a regular basis, who have not been trained to use robots and who do not have the technical background to know how robotics technology works.
Researchers in Embodied Cognition for Human-Robot Interaction are investigating in the necessary cognitive skills for robots to interact with humans in a natural and socially appropriate way. The research in this field has two parts to it. On one hand, there is the technical challenge of designing complex robot architectures that combine software components for multimodal input recognition, decision making, and multimodal output generation with appropriate robot hardware. On the other hand, there is the challenge to study the human factors of human-robot interaction. What appearance should a robot have to support a given task? Should the robot show social behaviour when interacting with humans? How do humans perceive working together with robots depending on task context and application area?
In this talk, I will give an overview of past and present research in Embodied Cognition for Human-Robot Interaction, showing its relevance to different application areas such us manufacturing, social interaction, and nuclear decommissioning. I will also attempt to look into the future of the field and discuss research questions that still need to be addressed.Short Biography
Dr. Manuel Giuliani is Professor in Embedded Cognitive AI for Robotics at the Bristol Robotics Laboratory (BRL), University of the West of England, Bristol. At BRL, he leads the ECHOS group (Embodied Cognition for Human RObot InteractionS). He received a Master of Arts in computational linguistics from Ludwig-Maximilian-University Munich, a Master of Science, and a PhD in computer science from Technical University Munich. Currently, he is Co-Investigator on the EPSRC-funded projects NCNR (National Centre for Nuclear Robotics), RNE (Robotics for Nuclear Environments), and DigiTOP (Digital Toolkit for Optimisation of Operators and Technology in Manufacturing Partnerships). In the past, he worked on the European projects JAST (Joint Action Science and Technology), JAMES (Joint Action for Multimodal Embodied Social Systems), ReMeDi (Remote Medical Diagnostician), the Cluster of Excellence CoTeSys (Cognition for Technical Systems), and the Austrian Christian-Doppler-Laboratory "Contextual Interfaces". Before going to Bristol, Manuel worked at the Technical University of Munich, fortiss GmbH in Munich, and the Center for Human-Computer Interaction at the University of Salzburg, where he led the Human-Robot Interaction group. His research interests include human-robot interaction, social robotics, natural language processing, multimodal fusion, multimodal output generation, augmented and virtual reality interfaces, and embedded cognitive robot architectures.
- Professor Robert Babuska, Professor of Intelligent Control and Robotics, TU Delft, Netherlands
Professor of Intelligent Control and Robotics, Delft University of Technology, The Netherlands
Talk Title: Nonlinear Control Design Through Reinforcement Learning: Challenges and Open Issues
Abstract
Reinforcement Learning (RL) algorithms provide a way to optimally solve dynamic decision-making and control problems. Recent progress in deep learning has enabled RL to scale to problems that were previously intractable. Notable examples include complex board games, such as Go, or tasks with high-dimension visual inputs, such as video games or robots learning directly from camera inputs. The ability to learn control policies from scratch is an undisputable advantage of RL, especially for problems where it is difficult or impossible to design a controller in advance, for instance because one cannot rely on a mathematical model of the system to be controlled. However, for RL to become a standard control design tool, many challenges need to be addressed. For instance, approaches based on deep neural networks suffer from the lack of reproducibility, caused by nondeterminism during the training process. In addition, the interpolation and extrapolation properties of the function approximators involved in RL may adversely affect the control performance and thorough comparisons with alternative control design methods are lacking. The focus of this talk is on the use of reinforcement learning as a tool for feedback control design to improve the closed-loop performance of nonlinear systems. We will address the aspects of value function and policy approximation, using methods ranging from standard basis function approximators, through deep neural networks to our new work showing how to incorporate analytical models generated by means of symbolic regression. The talk will include examples of nonlinear control problems that can be successfully solved by reinforcement learning as well as by alternative methods and will illustrate some of the challenges this exciting field of research is currently facing.
Short Biography
Prof. dr. Robert Babuska, MSc received the M.Sc. (Hons.) degree in control engineering from the Czech Technical University in Prague, in 1990, and the Ph.D. (cum laude) degree from TU Delft, the Netherlands, in 1997. He has had faculty appointments with the Czech Technical University in Prague and with the Electrical Engineering Faculty, TU Delft. Currently, he is a full professor of Intelligent Control and Robotics at TU Delft, Faculty 3mE, Department of Cognitive Robotics. In the past, he made seminal contributions also to the field of nonlinear control and identification with the use of fuzzy modeling techniques. His current research interests include reinforcement learning, adaptive and learning robot control, nonlinear system identification and state-estimation. He has been involved in the applications of these techniques in various fields, ranging from process control to robotics and aerospace.
- Dr Coorous Mohtadi, Senior Academic Technical Specialist, Mathworks
Senior Academic Technical Specialist, Mathworks
Talk Title: Are you ready for AI? Is AI ready for you?
Abstract
AI, or Artificial Intelligence, is powering a massive shift in the roles that computers play in our personal and professional lives. Most technical organizations expect to gain or strengthen their competitive advantage through the use of AI. But are you in a position to fulfill that expectation, to transform your research, your products, or your business using AI?
We look at the some of the techniques that compose AI (deep learning, computer vision, robotics, and more), enabling you to identify opportunities to leverage it in your work. You will also learn how MATLAB® and Simulink® are giving engineers and scientists AI capabilities that were previously available only to highly-specialized software developers and data scientists.Short Biography
Coorous Mohtadi is the EMEA Manager of MathWorks technical specialist team supporting universities focusing on the application of MATLAB and Simulink in laboratories and curriculum development. He is interested in finding synergies between industry and academia. He has been supporting research, design and development in universities and industry for the last 11 years at MathWorks.
Prior to joining MathWorks in 2007, Coorous was the European technical manager for temperature, process control, and component products at Omron Electronics Europe and the chief control engineer at Eurotherm Controls. During 1980s he was also a postdoctoral research fellow at University of Oxford, U.K. and University of Alberta, Canada. Coorous holds a D.Phil. in model-based predictive control and Masters in engineering science, both from University of Oxford. His paper on generalised predictive control has over 3500 citations and his industrial algorithms forms the core of extremely successful Eurotherm Controls 2000 series of products.
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