Learning MPC in Autonomous Systems

Learning MPC in Autonomous Systems

Speaker Name: 
Francesco Borrelli
Speaker Title: 
Howard Penn Brown Professor
Speaker Organization: 
UC Berkeley
Start Time: 
Wednesday, October 23, 2019 - 1:30pm
End Time: 
Wednesday, October 23, 2019 - 3:00pm
E2 506
Ricardo Sanfelice



Forecasts play an increasingly important role in the next generation of autonomous and semi-autonomous systems. Applications include transportation, energy, manufacturing and healthcare systems. Predictions of systems dynamics, human behavior and environment conditions can improve safety and performance of the resulting system. However, constraint satisfaction, performance guarantees and real-time computation are challenged by the growing complexity of the engineered system, the human/machine interaction and the uncertainty of the environment where the system operates.  Our research over the past years has focused on predictive control design for autonomous systems performing iterative tasks.  In this talk I will first provide an overview of the theory and tools that we have developed for the systematic design of learning predictive controllers.  Then, I will focus on recent results on the use of data to efficiently formulate stochastic MPC problems which autonomously improve performance in iterative tasks.  Throughout the talk I will focus on autonomous cars to motivate our research and show the benefits of the proposed techniques.

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 a Ph.D 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 publications in the field of predictive control. He is the 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 interests are in the area of model predictive control and its application to automated driving and energy systems.