Sample-Based Learning Model Predictive Control

Explore a comprehensive lecture on Sample-Based Learning Model Predictive Control delivered by Francesco Borrelli from the University of California, Berkeley. Delve into the intersection of control, learning, and optimization as part of the 2020 series at the Institute for Pure & Applied Mathematics. Gain insights into the theory and tools developed for designing learning predictive controllers, with a focus on recent advancements in sample-based Learning Model Predictive Controller (LMPC) for constrained uncertain linear systems. Discover the design principles of safe sets and value functions that ensure safety and performance improvement, and learn how these concepts can be approximated using noisy historical data. Throughout the 48-minute talk, examine real-world applications in autonomous cars and solar power plants to understand the practical benefits of these innovative techniques. Access additional resources and information at www.mpc.berkeley.edu to further expand your knowledge in this cutting-edge field.