Data-Driven Control - Overview

Explore an overview lecture on data-driven control, delving into how machine learning optimization can be utilized to uncover models and effective controllers directly from data. Learn about the intersection of control theory and machine learning, examining the motivation, challenges, and limitations of this approach. Gain insights into the application of machine learning techniques in control systems and discover the potential of data-driven methods in engineering and science. Based on Chapters 9 & 10 of "Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control" by Brunton and Kutz, this 25-minute talk provides a foundation for understanding the emerging field of data-driven control.

Introduction
Motivation
Challenges
Limitations
Control Theory
Machine Learning
Machine Learning Control