March 08, 2013, Webb 1100

Forecasts will play an increasingly important role in the the next generation of autonomous and semi-autonomous systems. Applications include  transportation, energy, manufacturing and healthcare systems. There is a widespread agreement that predictions of systems dynamics, human behavior and environment conditions can improve safety and performance of the  resulting system. However, in this context, constraint satisfaction, performance guarantees and real-time computation are  challenged by the (1) growing complexity of the engineered system, (2) uncertainty in the human/machine interaction and (3) uncertainty of the environment where the system operates. In this talk I will present the theory and tools that we have developed over the past ten years for the systematic design of predictive controllers for uncertain linear and nonlinear systems. I will first provide an overview of our theoretical efforts. Then, I will focus on our recent results for addressing constraints satisfaction and real-time computation in large-scale networked systems. Throughout the talk I will use two applications to motivate our research and show the benefits of the proposed techniques: Safe Autonomous Cars and Green Intelligent Buildings.

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 an Associate Professor at the Department of Mechanical Engineering of the University of California at Berkeley, USA.

He is the author of more than seventy publications in the field of predictive control. He is author of the book Constrained Optimal Control of Linear and Hybrid Systems published by Springer Verlag, the winner of the 2009 NSF CAREER Award. In 2008 he was appointed the chair of the IEEE technical committee on automotive control.He is the recipient of the 2012 IEEE Control System Technology Award. His research interests include constrained optimal control, model predictive control and its application to advanced automotive control and energy efficient building operation.

More info on: www.mpc.berkeley.edu