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Dane Sabo 2024-09-10 11:50:50 -04:00
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commit a7fa387b46
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2
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@ -88,6 +88,6 @@
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4. Qualifying Exam/2. Writing/0. QE Abstract.md
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@ -84,6 +84,13 @@ We suggest using a recent technology to more efficiently generate perturbed plan
[[QE Abstract For Dan]]
# Take 5
The goal of this research is to use a generative diffusion model to create unstructured perturbations of a nominal plant. For a given controller, an engineer can use robust control theory to find the set of allowable perturbations. This theory gives confidence that a model of a controller is robust, but verifying robustness on an implementation of a controller requires perturbed plants to be extracted from the set.
The goal of this research is to use a generative diffusion model to create unstructured perturbations of a nominal plant. If this research is successful, this diffusion model will accomplish three main tasks:
1. Generate Bode plots based on training data of example dynamic systems
2. Perturb a nominal plant in an unstructured manner with a controllable difference between perturbed and nominal plants
3. Approximate a set of controllable plants by generating a large number of perturbed examples
We suggest using a recent technology to more efficiently generate perturbed plants. The diffusion generative model has shown great promise in creating novel and realistic samples from training data. This model can 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.
The diffusion generative model has shown great promise in creating novel and realistic samples from training data. This research will train a generative model to create Bode plots of transfer functions. This trained model will then be given a warm start of nominal plant as an input, and then generate a limitless number of unique perturbed plants for controller validation. Once created, these perturbed plants can be evaluated if the belong in the set of controllable plants. We create several perturbed plants
For a given controller, an engineer can use robust control theory to find the set of allowable perturbations. This theory gives confidence that a model of a controller is robust, but verifying robustness on an implementation of a controller requires perturbed plants to be extracted from the set.
This model can reduce the effort required of creating perturbed plants.