91 lines
3.3 KiB
TeX
91 lines
3.3 KiB
TeX
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\hypersetup{
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pdftitle={QE Abstract For Dan},
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hidelinks,
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pdfcreator={LaTeX via pandoc}}
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\title{QE Abstract For Dan}
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\author{}
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\date{}
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\begin{document}
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\maketitle
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\textbf{Diffusion Generative Models For Unstructured Uncertainty
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Perturbations}
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Real world control systems operate on physical plants that can have
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different dynamics than a nominal model. This discrepancy is called a
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perturbation, and can affect controller performance. The field of robust
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control creates a way to establish a set of allowable perturbations for
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a given plant, controller, and design requirements. We can make
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guarantees that a controller meets performance or safety criterion when
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the real plant does not perfectly match the nominal model.
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A model controller can be proven to control a set of plants, but a real
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controller can only control one plant at a time. Validating robustness
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for a real controller requires extracted elements of the perturbed set,
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which can be deceptively difficult to create. Perturbed plants are
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commonly generated by using a structured uncertainty, where an engineer
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creates distributed ranges for system parameters. These distributions
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are then sampled and used to create a perturbed plant. This is an
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knowledge intensive and time consuming process.
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We suggest using generative artificial intelligence to efficiently
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create perturbed plants. The diffusion generative model has shown great
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promise in creating novel and realistic samples from training data. This
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model can be used to remove the laborious effort of creating perturbed
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plants. We suggest training a generative model to create Bode plots of
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transfer functions. This trained model will then be given a warm start
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with the nominal plant as an input, with which it will then be able to
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generate a limitless number of unique perturbed plants for controller
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validation.
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\textbf{STATS: 249 / 250 words}
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\end{document}
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