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Dane Sabo 2024-08-30 16:51:15 -04:00
parent bd5752def8
commit c17572d879
6 changed files with 41 additions and 11 deletions
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2
.obsidian/app.json vendored
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@ -5,7 +5,7 @@
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.obsidian/workspace.json vendored
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"file": "4. Qualifying Exam/2. Writing/QE Abstract.md",
"file": "QE Abstract For Dan.md",
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@ -107,7 +107,7 @@
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@ -124,7 +124,7 @@
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@ -147,7 +147,7 @@
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@ -203,8 +203,10 @@
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5
3-99 Research/6. Researching Techniques/Highlighting Colors and What they Mean.md
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@ -6,11 +6,12 @@ F9: Remove Highligh
<mark style="background: #FF5582A6;">Red</mark>: I think this is wrong
## For editing in Obsidian:
<mark style="background: #FF5582A6;">Red</mark>: This shouldn't even be here
<mark style="background: #FFF3A3A6;">Yellow</mark>: Important
<mark style="background: #ABF7F7A6;">Cyan</mark>: This is the topic
<mark style="background: #FFB8EBA6;">Pink</mark>: The (p)oint
<mark style="background: #FFB86CA6;">Orange</mark>: I think this is weak.
<mark style="background: #FF5582A6;">Red</mark>: This shouldn't even be here
<mark style="background: #FFF3A3A6;">Yellow</mark>: Important words, don't overuse
<mark style="background: #ADCCFFA6;">Blue</mark>: This needs a citation.
<mark style="background: #BBFABBA6;">Green</mark>:
<mark style="background: #CACFD9A6;">Gray</mark>: This needs more explanation
<mark style="background: #D2B3FFA6;">Purple</mark>:

22
4. Qualifying Exam/2. Writing/QE Abstract.md
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@ -61,4 +61,24 @@ While a model of a controller can be proven to control a set of plants, a real c
We suggest using a new technology to more efficiently generate perturbed plants. The diffusion generative model has shown great promise in creating novel and realistic samples from training data. 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 perturbed plants. This model can remove the laborious effort of creating perturbed plants.
**STATS: 250 words!**
**STATS: 250 words!**
## Edits
<mark style="background: #ABF7F7A6;">Real world control systems do not operate on nominal plants, but instead control a physical plant that has slightly different dynamics.</mark> This discrepancy is called a perturbation, and can affect controller performance. The field of robust control creates a way to establish set of allowable perturbations for a given plant, controller, and design requirements. <mark style="background: #FFB8EBA6;">As a result, we can make guarantees about the ability of a controller to meet performance or safety criterion when our model of the plant is not correct. </mark> [^7]
<mark style="background: #ABF7F7A6;">While a model of a controller can be proven to control a set of plants, a real controller can only be tested controlling one plant at a time. Validating this real controller requires extracted elements of the perturbed set,</mark><mark style="background: #ABF7F7A6;"> which can be deceptively difficult to create</mark>. Perturbed plants <mark style="background: #FFF3A3A6;">commonly are</mark>[^8] generated by using structured uncertainty, where an engineer <mark style="background: #FFF3A3A6;">attributes probability distributions to system parameters</mark>[^9]. These distributions are sampled, and then are used to create a perturbed plant. <mark style="background: #FFB8EBA6;">This is an expertise intense process.</mark>
<mark style="background: #ABF7F7A6;">We suggest using a new technology to more efficiently generate perturbed plants. The diffusion generative model has shown great promise in creating novel and realistic samples from training data.</mark> <mark style="background: #FFB8EBA6;">This model can remove the laborious effort of creating perturbed plants.</mark> 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 perturbed plants. ~~This model can remove the laborious effort of creating perturbed plants.~~
[^7]: Weak ass point.
[^8]:Switch order
[^9]: Maybe reverse? ' creates system parameters as probability distributions?' I like that better. Better stress position usage.
# Take 4
## Attempt
Real world control systems do not operate on nominal plants, but instead control a physical plant that has slightly different dynamics. This discrepancy is called a perturbation, and can affect controller performance. The field of robust control creates a way to establish 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.
While a model of a controller can be proven to control a set of plants, a real controller can only be tested controlling one plant at a time. Validating this 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 sampled and then are used to create a perturbed plant. This is an expertise intense process.
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.
**STATS: 250 / 250 words**
[[QE Abstract For Dan]]

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QE Abstract For Dan.md Normal file
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**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 in 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 sampled and then used to create a perturbed plant. This is an knowledge intensive and time consuming process.
We suggest using a 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.
**STATS: 250 / 250 words**