Francesco Borrelli of University of California at Berkeley

The complexity of modern autonomous systems has grown exponentially in the past decade. Today's end-to-end control demonstration have reached an impressive level of performance with apparently no modeling effort. Yet, delivering high performance autonomy which is safe despite environment uncertainty seems to be critically linked to the ability of modeling and predicting the uncertain environment.
Our research over the past decade has focused on control design for autonomous systems which systematically incorporate predictions and learning while guaranteeing safety. In this talk I will provide an overview of the theory and tools that we have developed in this area, address comparison with model-free approaches,  and discuss key open questions.

Biography:

Francesco Borrelli received the 'Laurea' degree in computer science engineering in 1998 from the University of Naples 'Federico II', Italy. In 2002 he received the PhD from the Automatic Control Laboratory at ETH-Zurich, Switzerland. He is currently a Professor at the Department of Mechanical Engineering of the University of California at Berkeley, USA. He is the author of more than two hundred publications in the field of predictive control. He is author of the book Predictive Control published by Cambridge University Press, the winner of the 2009 NSF CAREER Award and the winner of the 2012 IEEE Control System Technology Award. In 2016 he was elected IEEE fellow. In 2017 he was awarded the Industrial Achievement Award by the International Federation of Automatic Control (IFAC) Council. Since 2004 he has served as a consultant for major international corporations. He was the founder and CTO of BrightBox Technologies Inc, a company focused on cloud-computing optimization for autonomous systems. He was the co-director of the Hyundai Center of Excellence in Integrated Vehicle Safety Systems and Control at UC Berkeley.  He is the founder of WideSense Inc., a company focused on E-Mobility. His research interests are in the area of model predictive control and its application to automated driving and energy systems.