Monte and Usha Ahuja Distinguished Lecture Series - Speaker: Francesco Borrelli
Learning and Forecasts in Autonomous Systems
The complexity of modern autonomous systems has grown exponentially in the past decade. Today’s control engineers need to deliver high performance autonomy that is safe despite environment uncertainty, is able to interact effectively with humans, and improve 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 a task. In addition, historical and real-time data can be used to bound forecast uncertainty, learn model parameters, and allow the system to adapt to new tasks.
In this talk I will first provide an overview of the theory and tools that we have developed for designing 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 focus on autonomous cars to motivate our research and show the benefits of the proposed techniques.
About the Speaker
Francesco Borrelli received the PhD degree 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. He is the author of more than one hundred fifty publications in the field of predictive control, and the winner of an NSF CAREER Award and the 2012 IEEE Control System Technology Award. He is an IEEE fellow and received the Industrial Achievement Award from the International Federation of Automatic Control Council in 2017. He has served as a consultant for major international corporations, and 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.