Prof. Melanie Zeilinger
Institute for Dynamic Systems and Control
ETH Zurich, Switzerland
Friday, 2017-06-02 14:00
IST-Seminar-Room 2.268 - Pfaffenwaldring 9 - Campus Stuttgart-Vaihingen
Demanding performance requirements combined with increasing complexity, uncertainty and human interaction in many emerging application problems, e.g. in robotic, transportation, or power systems, are pushing traditional control methods to their limits. A new opportunity to address these challenges is offered by sensor technologies with the ability to collect large amounts of data online. While machine learning provides powerful techniques to analyze and utilize such large-scale data, safety concerns when integrating them in a closed-loop, automated decision-making process represent a key limitation for leveraging their potential.
In this talk, I will discuss some of our recent work towards a controller that utilizes online data to enhance system performance, while ensuring satisfaction of safety conditions at all times. I will present a technique based on Gaussian processes and show how a predictive controller can be systematically tailored online to the particular system at hand providing a high performance controller with reduced development times. I will then address aspects of safety and introduce the idea of a safety wrapper that ensures satisfaction of constraints for any online control scheme. The key novelty is the learning capability of the wrapper itself, utilizing data to find the largest region of safe operation where a performance-maximizing controller can be employed.
Melanie Zeilinger is an Assistant Professor at the Department of Mechanical and Process Engineering at ETH Zurich, where she is leading the Intelligent Control Systems group. From 2012 to 2015 she was a Postdoctoral Researcher and Marie Curie fellow in a joint program with the University of California at Berkeley, USA, and the Max Planck Institute for Intelligent Systems in Tuebingen, Germany. From 2011 to 2012 she was a postdoctoral fellow at the École Polytechnique Fédérale de Lausanne (EPFL), Switzerland. She received the Ph.D. degree in Electrical Engineering from ETH Zurich in 2011, and the diploma in Engineering Cybernetics from the University of Stuttgart in Germany in 2006. She is the recipient of several awards, including the ETH Medal for her PhD thesis and a Marie Curie Fellowship for Career Development by the European Commission. Her research interests are centered around real-time and distributed control and optimization, as well as safe learning-based control, with applications to energy distribution and management systems and human-in-the-loop control.