The complexity of modern autonomous systems has grown exponentially in the past decade. Today's control engineers need to deliver high performance autonomy which is safe despite environment uncertainty, is able to effectively interact with humans, and improves system performance by using data processed on local and remote computing platforms.
Employing predictions of system dynamics, human behavior and environment components can facilitate such task. In addition, historical and real-time data can be used to bound forecasts uncertainty, learn model parameters and allow the system to adapt to new tasks.
Our research over the past decade has focused on control design for autonomous systems which systematically incorporate predictions and learning. In this talk I will first provide an overview of the theory and tools that we have developed for the designing of learning predictive controllers. Then, I will focus on recent results that use data to efficiently formulate stochastic control problems which autonomously improve performance in iterative tasks. Throughout the talk I will show the benefits of the proposed techniques by presenting real-world implementations in the area of connected and automated cars, collaborative robotics and large scale solar power plants.
More info on: www.mpc.berkeley.edu
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 one hundred fifty 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 is the co-director of the Hyundai Center of Excellence in Integrated Vehicle Safety Systems and Control at UC Berkeley. His research interest are in the area of model predictive control and its application to automated driving and energy systems.