  The rapid evolution of Cyber-Physical Systems (CPS) across various domains
like mobility systems, networked control systems, sustainable manufacturing,
smart power grids, and the Internet of Things necessitates innovative solutions
that merge control and learning [1]. Traditional model-based control
methodologies often fail to adapt to the dynamism and complexity of modern CPS.
This report outlines a comprehensive approach undertaken by the Information and
Decision Science (IDS) Lab, focusing on integrating data-driven techniques with
control strategies to enhance CPS performance, particularly in the context of
energy efficiency and environmental impact. CPS are intricate networks where
physical and software components are deeply intertwined, operating as systems
of systems. These systems are characterized by their informationally
decentralized nature, posing significant challenges in optimization and
control. Classical control methods depend heavily on precise models, which
often do not capture the full complexity of real-world CPS. As these systems
generate large volumes of real-time data, there is a growing need for control
algorithms that can leverage this data effectively. The IDS Lab is at the
forefront of developing such data-driven approaches for CPS.
