Francesco Borrelli: "Sample-Based Learning Model Predictive Control"

Intersections between Control, Learning and Optimization 2020

"Sample-Based Learning Model Predictive Control"
Francesco Borrelli - University of California, Berkeley (UC Berkeley)

Abstract: Our research over the past years has focused on learning 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 sample-based Learning Model Predictive Controller (LMPC) for constrained uncertain linear systems. I will first introduce the design of the safe set and value function used to guarantee safety and performance improvement. Then, I will show how these quantities can be approximated using noisy historical data. Throughout the talk I will focus on autonomous cars and solar power plants to motivate our research and show the benefits of the proposed techniques.
More info on: www.mpc.berkeley.edu

Institute for Pure and Applied Mathematics, UCLA
February 26, 2020

For more information: http://www.ipam.ucla.edu/lco2020