Paul Van den Hof, Dec 2018

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Paul van den Hof is Full Professor and Chair of the Control Systems (CS) Group at the Department of Electrical Engineering. He is interested in data-driven modeling, control and optimization of dynamic systems in several technological fields: industrial process control, oil reservoir engineering, high-tech mechatronic and cyber-physical systems, etc. His focus is the development of fundamental techniques, such as data-driven modeling, closed-loop and control-oriented identification and data analytics, experimental design and performance monitoring, and model-based control, monitoring and optimization.


  • 10 am: Richard Murray, 107 Steele Lab
  • 10:30 am: Petter Nilsson, 110 Steele
  • 11:00 am: Yuxiao Chen, Thomas Gurriet
  • 11:45 am: Lunch with Aaron Ames, Petter Nilsson, Thomas Gurriet, Yuxiao Chen, meet in 266 Gates-Thomas
  • 1:00 pm: Seminar, 106 Annenberg
  • 2:00 pm: Soon-Jo Chung, 106 Annenberg
  • 2:45 pm: Michaelle Mayalu, 2nd Floor lounge, Annenberg
  • 3:30 pm: Richard Murray, 107 Steele Lab
  • 4:00 pm: Chelsea and Ayush, 2nd floor lounge, Annenberg
  • 4:45 pm: Open (if nothing else available)
  • 5:30 pm: Depart


Data-driven modeling in linear dynamic networks
Friday, December 7th at 1pm, 106 Annenberg

In many areas of science and technology, the complexity of dynamic systems that are being considered, grows beyond the level of single systems into interconnected networks of dynamic systems. In control and optimization this has led to the development of decentralized and distributed algorithms for control/optimization, as e.g. in multi-agent systems. From the modelling perspective, data-driven modelling tools are typically developed for relatively simple open-loop and closed-loop structures, while the opportunities for big data handling in the current data science era, are becoming abundant. As a result there is a strong need for the development of data-driven modelling tools for large-scale interconnected dynamic networks. In this seminar we will highlight the main developments and challenges in this area. Besides setting up a modelling framework, we will address problems of local identification of a particular part of the network, including the selection of the appropriate signals to be measured. The concept of network identifiability is highlighted and the role of structural properties of the network, in terms of its topology/graph, is given strong attention. It is also shown how classical closed-loop identification methods need to be generalized to be able to cope with the new situations.