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  pdftitle={QE Abstract For Dan},
  hidelinks,
  pdfcreator={LaTeX via pandoc}}

\title{QE Abstract For Dan}
\author{}
\date{}

\begin{document}
\maketitle

\textbf{Diffusion Generative Models For Unstructured Uncertainty
Perturbations}

Real world control systems operate on physical plants that can have
different dynamics than a nominal model. This discrepancy is called a
perturbation, and can affect controller performance. The field of robust
control creates a way to establish a set of allowable perturbations for
a given plant, controller, and design requirements. We can make
guarantees that a controller meets performance or safety criterion when
the real plant does not perfectly match the nominal model.

A model controller can be proven to control a set of plants, but a real
controller can only control one plant at a time. Validating robustness
for a real controller requires extracted elements of the perturbed set,
which can be deceptively difficult to create. Perturbed plants are
commonly generated by using a structured uncertainty, where an engineer
creates distributed ranges for system parameters. These distributions
are then sampled and used to create a perturbed plant. This is an
knowledge intensive and time consuming process.

We suggest using generative artificial intelligence to efficiently
create perturbed plants. The diffusion generative model has shown great
promise in creating novel and realistic samples from training data. This
model can be used to remove the laborious effort of creating perturbed
plants. We suggest training a generative model to create Bode plots of
transfer functions. This trained model will then be given a warm start
with the nominal plant as an input, with which it will then be able to
generate a limitless number of unique perturbed plants for controller
validation.

\textbf{STATS: 249 / 250 words}

\end{document}
