Linear Operator Theoretic Framework for Data-Driven Optimal Control

Linear Operator Theoretic Framework for Data-Driven Optimal Control

Explore a 24-minute lecture from the Fourth Symposium on Machine Learning and Dynamical Systems, presented by Umesh Vaidya of Clemson University at the Fields Institute. Delve into the Linear Operator Theoretic Framework for Data-Driven Optimal Control, gaining insights into cutting-edge approaches that merge machine learning techniques with dynamical systems theory. Discover how this framework can be applied to solve complex control problems using data-driven methods, potentially revolutionizing fields such as robotics, autonomous systems, and process control.